Excess years of life lost to COVID-19 and other causes of death by sex,neighbourhood deprivation,and region in England and Wales during 2020: A registry-based study

BackgroundDeaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups.Methods and findingsWe used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording.ConclusionsIn this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL.

In a registry-based study, Evangelos Kontopantelis and colleagues examine the excess years of life lost to COVID-19 and other causes of death by sex, neighbourhood deprivation and region in England & Wales during 2020.     

Factors associated with excess all-cause mortality in the first wave of the COVID-19 pandemic in the UK: A time series analysis using the Clinical Practice Research Datalink

BackgroundExcess mortality captures the total effect of the Coronavirus Disease 2019 (COVID-19) pandemic on mortality and is not affected by misspecification of cause of death. We aimed to describe how health and demographic factors were associated with excess mortality during, compared to before, the pandemic.Methods and findingsWe analysed a time series dataset including 9,635,613 adults (≥40 years old) registered at United Kingdom general practices contributing to the Clinical Practice Research Datalink. We extracted weekly numbers of deaths and numbers at risk between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during Wave 1 of the UK pandemic (5 March to 27 May 2020) compared to the prepandemic period was estimated using seasonally adjusted negative binomial regression models. Relative rates (RRs) of death for a range of factors were estimated before and during Wave 1 by including interaction terms. We found that all-cause mortality increased by 43% (95% CI 40% to 47%) during Wave 1 compared with prepandemic. Changes to the RR of death associated with most sociodemographic and clinical characteristics were small during Wave 1 compared with prepandemic. However, the mortality RR associated with dementia markedly increased (RR for dementia versus no dementia prepandemic: 3.5, 95% CI 3.4 to 3.5; RR during Wave 1: 5.1, 4.9 to 5.3); a similar pattern was seen for learning disabilities (RR prepandemic: 3.6, 3.4 to 3.5; during Wave 1: 4.8, 4.4 to 5.3), for black or South Asian ethnicity compared to white, and for London compared to other regions. Relative risks for morbidities were stable in multiple sensitivity analyses. However, a limitation of the study is that we cannot assume that the risks observed during Wave 1 would apply to other waves due to changes in population behaviour, virus transmission, and risk perception.ConclusionsThe first wave of the UK COVID-19 pandemic appeared to amplify baseline mortality risk to approximately the same relative degree for most population subgroups. However, disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.

Helen Strongman and colleagues investigate the health and demographic factors associated with excess mortality during, as compared to before, the COVID-19 pandemic.     

Healthcare utilization and maternal and child mortality during the COVID-19 pandemic in 18 low- and middle-income countries: An interrupted time-series analysis with mathematical modeling of administrative data

BackgroundThe Coronavirus Disease 2019 (COVID-19) pandemic has had wide-reaching direct and indirect impacts on population health. In low- and middle-income countries, these impacts can halt progress toward reducing maternal and child mortality. This study estimates changes in health services utilization during the pandemic and the associated consequences for maternal, neonatal, and child mortality.Methods and findingsData on service utilization from January 2018 to June 2021 were extracted from health management information systems of 18 low- and lower-middle-income countries (Afghanistan, Bangladesh, Cameroon, Democratic Republic of the Congo (DRC), Ethiopia, Ghana, Guinea, Haiti, Kenya, Liberia, Madagascar, Malawi, Mali, Nigeria, Senegal, Sierra Leone, Somalia, and Uganda). An interrupted time-series design was used to estimate the percent change in the volumes of outpatient consultations and maternal and child health services delivered during the pandemic compared to projected volumes based on prepandemic trends. The Lives Saved Tool mathematical model was used to project the impact of the service utilization disruptions on child and maternal mortality. In addition, the estimated monthly disruptions were also correlated to the monthly number of COVID-19 deaths officially reported, time since the start of the pandemic, and relative severity of mobility restrictions. Across the 18 countries, we estimate an average decline in OPD volume of 13.1% and average declines of 2.6% to 4.6% for maternal and child services. We projected that decreases in essential health service utilization between March 2020 and June 2021 were associated with 113,962 excess deaths (110,686 children under 5, and 3,276 mothers), representing 3.6% and 1.5% increases in child and maternal mortality, respectively. This excess mortality is associated with the decline in utilization of the essential health services included in the analysis, but the utilization shortfalls vary substantially between countries, health services, and over time. The largest disruptions, associated with 27.5% of the excess deaths, occurred during the second quarter of 2020, regardless of whether countries reported the highest rate of COVID-19-related mortality during the same months. There is a significant relationship between the magnitude of service disruptions and the stringency of mobility restrictions. The study is limited by the extent to which administrative data, which varies in quality across countries, can accurately capture the changes in service coverage in the population.ConclusionsDeclines in healthcare utilization during the COVID-19 pandemic amplified the pandemic’s harmful impacts on health outcomes and threaten to reverse gains in reducing maternal and child mortality. As efforts and resource allocation toward prevention and treatment of COVID-19 continue, essential health services must be maintained, particularly in low- and middle-income countries.

Tashrik Ahmed and co-workers study health-care use and maternal and child health outcomes across low- and lower-middle-income countries during the COVID-19 pandemic.     

Early warning of vulnerable counties in a pandemic using socio-economic variables

In the U.S. in early 2020, heterogenous and incomplete county-scale data on COVID-19 hindered effective interventions in the pandemic. While numbers of deaths can be used to estimate actual number of infections after a time lag, counties with low death counts early on have considerable uncertainty about true numbers of cases in the future. Here we show that supplementing county-scale mortality statistics with socioeconomic data helps estimate true numbers of COVID-19 infections in low-data counties, and hence provide an early warning of future concern. We fit a LASSO negative binomial regression to select a parsimonious set of five predictive variables from thirty-one county-level covariates. Of these, population density, public transportation use, voting patterns and % African-American population are most predictive of higher COVID-19 death rates. To test the model, we show that counties identified as under-estimating COVID-19 on an early date (April 17) have relatively higher deaths later (July 1) in the pandemic.     

Disparities in distribution of COVID-19 vaccines across US counties: A geographic information system–based cross-sectional study

BackgroundThe US Centers for Disease Control and Prevention has repeatedly called for Coronavirus Disease 2019 (COVID-19) vaccine equity. The objective our study was to measure equity in the early distribution of COVID-19 vaccines to healthcare facilities across the US. Specifically, we tested whether the likelihood of a healthcare facility administering COVID-19 vaccines in May 2021 differed by county-level racial composition and degree of urbanicity.Methods and findingsThe outcome was whether an eligible vaccination facility actually administered COVID-19 vaccines as of May 2021, and was defined by spatially matching locations of eligible and actual COVID-19 vaccine administration locations. The outcome was regressed against county-level measures for racial/ethnic composition, urbanicity, income, social vulnerability index, COVID-19 mortality, 2020 election results, and availability of nontraditional vaccination locations using generalized estimating equations.Across the US, 61.4% of eligible healthcare facilities and 76.0% of eligible pharmacies provided COVID-19 vaccinations as of May 2021. Facilities in counties with >42.2% non-Hispanic Black population (i.e., > 95th county percentile of Black race composition) were less likely to serve as COVID-19 vaccine administration locations compared to facilities in counties with <12.5% non-Hispanic Black population (i.e., lower than US average), with OR 0.83; 95% CI, 0.70 to 0.98, p = 0.030. Location of a facility in a rural county (OR 0.82; 95% CI, 0.75 to 0.90, p < 0.001, versus metropolitan county) or in a county in the top quintile of COVID-19 mortality (OR 0.83; 95% CI, 0.75 to 0.93, p = 0.001, versus bottom 4 quintiles) was associated with decreased odds of serving as a COVID-19 vaccine administration location.There was a significant interaction of urbanicity and racial/ethnic composition: In metropolitan counties, facilities in counties with >42.2% non-Hispanic Black population (i.e., >95th county percentile of Black race composition) had 32% (95% CI 14% to 47%, p = 0.001) lower odds of serving as COVID administration facility compared to facilities in counties with below US average Black population. This association between Black composition and odds of a facility serving as vaccine administration facility was not observed in rural or suburban counties. In rural counties, facilities in counties with above US average Hispanic population had 26% (95% CI 11% to 38%, p = 0.002) lower odds of serving as vaccine administration facility compared to facilities in counties with below US average Hispanic population. This association between Hispanic ethnicity and odds of a facility serving as vaccine administration facility was not observed in metropolitan or suburban counties.Our analyses did not include nontraditional vaccination sites and are based on data as of May 2021, thus they represent the early distribution of COVID-19 vaccines. Our results based on this cross-sectional analysis may not be generalizable to later phases of the COVID-19 vaccine distribution process.ConclusionsHealthcare facilities in counties with higher Black composition, in rural areas, and in hardest-hit communities were less likely to serve as COVID-19 vaccine administration locations in May 2021. The lower uptake of COVID-19 vaccinations among minority populations and rural areas has been attributed to vaccine hesitancy; however, decreased access to vaccination sites may be an additional overlooked barrier.

Inmaculada Hernandez and colleagues investigate the disparities in early-phase distribution of COVID-19 Vaccines across U.S. Counties.     

Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

BackgroundWith the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.Methods and findingsCounty-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R²); and (ii) effect estimates of each predictor.Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R². Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R² was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.ConclusionsSignificant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

Sasikiran Kandula and Jeffrey Shaman study population health and COVID-19 mortality in the United States.     

Social determinants of mortality from COVID-19: A simulation study using NHANES

BackgroundThe COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level.Methods and findingsWe estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%–59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%–71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% –27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%–23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents.ConclusionsSubstantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.

In this simulation study, Benjamin Seligman and colleagues explore socio-demographic factors associated with COVID-19 deaths in the US.     

Genetic tracing of HCoV-19 for the re-emerging outbreak of COVID-19 in Beijing,China

The ongoing pandemic of coronavirus disease 2019(COVID-19)caused by a novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2,also named as 2019-nCoV or HCoV-19)poses an unprecedented threat to public health(Zhu et al.,2020;Wang et al.,2020;Jiang et al.,2020).The novel HCoV-19 virus has rapidly spread into multiple countries across the world since it was first reported in December 2019.The World Health Organization(WHO)declared COVID-19 as a pandemic on 11th March 2020.As of 4th July,over 10 million confirmed COVID-19 cases have been reported in over 200 countries/regions with more than 0.5 million deaths,including 85,287 documented cases and 4,648 deaths in China(WHO,2020a).     

Spatiotemporal modeling of first and second wave outbreak dynamics of COVID-19 in Germany

The COVID-19 pandemic has kept the world in suspense for the past year. In most federal countries such as Germany, locally varying conditions demand for state- or county-level decisions to adapt to the disease dynamics. However, this requires a deep understanding of the mesoscale outbreak dynamics between microscale agent models and macroscale global models. Here, we use a reparameterized SIQRD network model that accounts for local political decisions to predict the spatiotemporal evolution of the pandemic in Germany at county resolution. Our optimized model reproduces state-wise cumulative infections and deaths as reported by the Robert Koch Institute and predicts the development for individual counties at convincing accuracy during both waves in spring and fall of 2020. We demonstrate the dominating effect of local infection seeds and identify effective measures to attenuate the rapid spread. Our model has great potential to support decision makers on a state and community politics level to individually strategize their best way forward during the months to come.

     

Colonoscopy screening and surveillance disparities during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic has increased barriers to accessing preventive healthcare. This study identifies populations disproportionately underrepresented in screening and surveillance colonoscopies during the COVID-19 pandemic.MethodsIn this single-center cohort study, colonoscopy procedures were reviewed during 6-month intervals before the pandemic (July 1, 2019 - December 31, 2019) and during the pandemic (July 1, 2020 - December 31, 2020 and January 1, 2021 - June 30, 2021). 7095 patients were categorized based on procedure indication, demographics, Charlson Comorbidity Index and Social Vulnerability Index (SVI). Statistics performed using VassarStats.Results2387 (2019) colonoscopies pre-pandemic and 2585 (2020) and 2123 (2021) during the pandemic were identified. There was a decrease in colonoscopies performed during months when COVID-19 cases peaked. The total number of average CRC risk patients presenting for first colonoscopy declined during the pandemic: 232 (10 %) pre-pandemic to 190 (7 %) in 2020, 145 (7 %) in 2021 (p < 0.001). Fewer of these patients presented from highly vulnerable communities, SVI > 0.8, during the pandemic, 39 in 2019 vs 16 in 2020 and 22 in 2021. Of all screening and surveillance patients, fewer presented from communities with SVI > 0.8 during the pandemic, 106 in 2019 versus 67 in 2020 and 77 in 2021.ConclusionIt is important to address the decline in CRC preventive care during this pandemic among average CRC risk first-time screeners and vulnerable community patients. An emphasis on addressing social determinants of health and establishing patients in gastroenterology clinics is imperative to promote future health in these populations.     

Achieving global mortality reduction targets and universal health coverage: The impact of COVID-19

Wenhui Mao and coauthors discuss possible implications of the COVID-19 pandemic for health aspirations in low- and middle-income countries.

Summary points

• The Coronavirus Disease 2019 (COVID-19) pandemic threatens progress toward a “grand convergence” in global health—universal reduction in deaths from infections and maternal and child health conditions to low levels—and toward achieving universal health coverage (UHC).
• Our analysis suggests that COVID-19 will exacerbate the difficulty of achieving grand convergence targets for tuberculosis (TB), maternal mortality, and, probably, for under-5 mortality. HIV targets are likely to be met.
• By 2035, our analysis suggests that the public sectors of low-income countries (LICs) would only be able to finance about a third of the costs of a package of 120 essential non-COVID-19 health interventions through domestic sources, unless the country increases significantly the priority assigned to the health sector; lower middle-income countries (LMICs) would likewise only be able to finance a little less than half.
• The likelihood of getting back on track for reaching grand convergence and UHC will depend on (i) how quickly COVID-19 vaccines can be deployed in LICs and LMICs; (ii) how much additional public sector health financing can be mobilized from external and domestic sources; and (iii) whether countries can rapidly strengthen and focus their health delivery systems.

     

The impact of COVID-19 on clinical research for Neglected Tropical Diseases (NTDs): A case study of bubonic plague

BackgroundAmong the many collaterals of the COVID-19 pandemic is the disruption of health services and vital clinical research. COVID-19 has magnified the challenges faced in research and threatens to slow research for urgently needed therapeutics for Neglected Tropical Diseases (NTDs) and diseases affecting the most vulnerable populations. Here we explore the impact of the pandemic on a clinical trial for plague therapeutics and strategies that have been considered to ensure research efforts continue.MethodsTo understand the impact of the COVID-19 pandemic on the trial accrual rate, we documented changes in patterns of all-cause consultations that took place before and during the pandemic at health centres in two districts of the Amoron’I Mania region of Madagascar where the trial is underway. We also considered trends in plague reporting and other external factors that may have contributed to slow recruitment.ResultsDuring the pandemic, we found a 27% decrease in consultations at the referral hospital, compared to an 11% increase at peripheral health centres, as well as an overall drop during the months of lockdown. We also found a nation-wide trend towards reduced number of reported plague cases.DiscussionCOVID-19 outbreaks are unlikely to dissipate in the near future. Declining NTD case numbers recorded during the pandemic period should not be viewed in isolation or taken as a marker of things to come. It is vitally important that researchers are prepared for a rebound in cases and, most importantly, that research continues to avoid NTDs becoming even more neglected.     

A linear mixed model to estimate COVID-19-induced excess mortality

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.     

Covid-19 in end-stage renal disease patients with renal replacement therapies: A systematic review and meta-analysis

BackgroundThe novel coronavirus (COVID-19), caused by SARS-CoV-2, showed various prevalence and case-fatality rates (CFR) among patients with different pre-existing chronic conditions. End-stage renal disease (ESRD) patients with renal replacement therapy (RRT) might have a higher prevalence and CFR due to reduced immune function from uremia and kidney tropism of SARS-CoV-2, but there was a lack of systematic study on the infection and mortality of the SARS-CoV-2 infection in ESRD patients with various RRT.Methodology/Principal findingsWe searched five electronic databases and performed a systematic review and meta-analysis up to June 30, 2020, to evaluate the prevalence and case fatality rate (CFR) of the COVID-19 infection among ESRD patients with RRT. The global COVID-19 data were retrieved from the international database on June 30, 2020, for estimating the prevalence and CFR of the general population as referencing points. Of 3,272 potential studies, 34 were eligible studies consisted of 1,944 COVID-19 confirmed cases in 21,873 ESRD patients with RRT from 12 countries in four WHO regions. The overall pooled prevalence in ESRD patients with RRT was 3.10% [95% confidence interval (CI) 1.25–5.72] which was higher than referencing 0.14% global average prevalence. The overall estimated CFR of COVID-19 in ESRD patients with RRT was 18.06% (95% CI 14.09–22.32) which was higher than the global average at 4.98%.ConclusionsThis meta-analysis suggested high COVID-19 prevalence and CFR in ESRD patients with RRT. ESRD patients with RRT should have their specific protocol of COVID-19 prevention and treatment to mitigate excess cases and deaths.     

No substantial excess all-cause mortality among cardiac implantable electronic device patients during the first COVID‑19 lockdown in the Leiden area

In the Netherlands, the coronavirus disease 2019 (COVID‑19) pandemic has resulted in excess mortality nationwide. Chronic heart disease patients are at risk for a complicated COVID‑19 course. The current study investigates all-cause mortality among cardiac implantable electronic device (CIED) patients during the first peak of the pandemic and compares the data to the statistics for the corresponding period in the two previous years. Data of adult CIED patients undergoing follow-up at the Leiden University Medical Centre were analysed. All-cause mortality between 1 March and 31 May 2020 was evaluated and compared to the data for the same period in 2019 and 2018. At the beginning of the first peak of the pandemic, 3,171 CIED patients (median age 70 years; 68% male; 41% ischaemic aetiology) were alive. Baseline characteristics of the 2019 (n = 3,216) and 2018 (n = 3,169) cohorts were comparable. All-cause mortality during the peak of the pandemic was 1.4% compared to 1.6% and 1.4% in the same period in 2019 and 2018, respectively (p = 0.84). During the first peak of the COVID‑19 pandemic, there was no substantial excess mortality among CIED patients in the Leiden area, despite the fact that this is group at high risk for a complicated course of a COVID‑19 infection. Strict adherence to the preventive measures may have prevented substantial excess mortality in these vulnerable patients.Supplementary InformationThe online version of this article (10.1007/s12471-021-01650-y) contains supplementary material, which is available to authorized users.     

COVID-19 and indirect health implications in Africa: Impact,mitigation measures,and lessons learned for improved disease control

Seth Inzaule and co-authors discuss implications of the COVID-19 pandemic for health in African countries.

Summary points

• Coronavirus Disease 2019 (COVID-19) and the mitigation measures taken to limit its spread have significantly disrupted other essential health services in Africa. This disruption has threatened the control of major high-burden diseases such as HIV, tuberculosis (TB), and malaria as well as the prevention of maternal and child mortality.
• While the 2020 WHO global reports for HIV, TB, and malaria show progress in control of these diseases in African countries, there are still significant gaps in meeting the global targets. Similarly, modeling studies predict that most African countries are unlikely to meet the Sustainable Development Goals (SDGs) 2030 targets for reductions in maternal and child mortality under the current rate of progress.
• Prediction models and emerging empirical data indicate that the implemented mitigation measures against COVID-19 such as travel restrictions and lockdowns as well as the repurposing of health resources and suspension of prevention programs such as immunizations will lead to an increase in new infections and deaths, significantly reversing the gains achieved in the control of these health challenges.
• A more comprehensive COVID-19 response that minimizes indirect deaths is therefore warranted in Africa. These include implementing WHO recommendations that limit contact with the clinic where possible, such as multimonths drug dispensing, self-testing, virtual platforms for case management, community- and home-based prevention, and care services such as home distribution of test kits, vaccines, treatment, and mosquito nets.
• This is in addition to ensuring effective implementation of COVID-19 infection prevention and control measures in healthcare facilities including providing healthcare workers with personal protective equipment and prioritizing them for COVID-19 immunization.
• There is also a need to incorporate aggressive recovery plans to reverse the lost gains in disease control efforts and put African countries back on course toward achieving the global targets. This includes leveraging on the wider COVID-19 response enablements such as the increased political will and global solidarity funding efforts to support a more comprehensive response that accounts for the indirect public health effects of the pandemic.

     

Impact of coronavirus disease 2019 on lung cancer patients: A meta-analysis

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic poses a great challenge to the treatment of lung cancer patients.Materials and methodsThe PubMed, Embase, and Web of Science databases were searched for studies published before March 15, 2022, and Stata 14.0 software was used to perform a meta-analysis with a random-effects model. The odds ratio (OR) along with the corresponding 95% confidence interval (CI) was reported.ResultsOur meta-analysis included 80 articles with 318,352 patients involved. The proportion of lung cancer patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 2.4% (95% CI: 0.02–0.03) prior to the Omicron variant outbreak. Among COVID-19 patients, those with lung cancer showed a higher mortality rate than those with other types of malignant solid tumors (OR = 1.82, 95% CI: 1.61–2.06) and non-cancer patients (OR = 4.67, 95% CI: 3.61–6.05); however, no significant difference was observed in the mortality rate between patients with lung cancer and those with hematologic malignancies (OR = 1.07, 95% CI: 0.85–1.33). SARS-CoV-2 infection significantly increased the mortality rate in lung cancer patients (OR = 8.94, 95% CI: 6.50–12.31). By contrast, the all-cause mortality rate in lung cancer patients (OR = 1.04, 95% CI: 0.69–1.57) and the proportion of patients diagnosed with advanced lung cancer (OR = 1.04, 95% CI: 0.85–1.27) did not significantly change before and after the pandemic.ConclusionsMore attention should be paid on improving the health of lung cancer patients during the COVID-19 pandemic.     

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R_t<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.     

Factores de riesgo asociados a fallecimiento por la variante ómicron de COVID-19: análisis retrospectivo con personas mayores de Canarias

Background and aimsSince the beginning of the COVID-19 pandemic, the elderly population has had the highest rates of complications and mortality. This study aimed to determine the influence of different risk factors on deaths due to the Omicron variant in the Canary Islands.Materials and methodsA retrospective observational study of 16,998 cases of COVID-19 over 40 years of age was conducted in the Canary Islands between August 1, 2022, and January 31, 2023. We extracted sociodemographic data (age and sex) and clinical data (death, vaccination history, hospital admission, previous diseases, and treatments).ResultsAmong the deaths, there was a higher proportion of males aged over 70 years, with diabetes, cardiovascular, renal, respiratory, and systemic diseases, and nursing home residents. Significant differences were observed in the number of doses of the vaccine. The multiple regression model showed that male sex (OR [95% CI] = 1.92 [1.42–2.58]), age (70–79 years, 9.11 [4.27–19.43]; 80–89 years, 21.72 [10.40–45.36]; 90–99 years, 66.24 [31.03–141.38]; 100 years or older, 69.22 [12.97–369.33]), being unvaccinated (6.96, [4.01–12.08]), or having the last dose administered at least 12 months before the diagnosis (2.38, [1.48–3.81]) were significantly associated with mortality.ConclusionsMultiple factors may increase the risk of mortality due to COVID-19 in the elderly population. In our study, we found that only three predictors can effectively explain the variability: older age, male sex, and not being vaccinated or last vaccination date prior to one year.