Category: Covid-19

Response variable

COVID-19 unvaccinated percentage (UP) is our chosen response variable. UP is calculated as the partial vaccination rate (PVR) subtracted from 100%, where the PVR is defined as the percentage of people in a county who had taken at least one dose of Pfizer (Comirnaty) or Moderna (Spikevax)16,24. As our goal is to deepen our understanding of vaccine hesitancy or vaccine refusal, we chose to define our variable as the percent of the population that did not receive any COVID-19 vaccine doses, rather than the percent fully vaccinated. Defining our variable as the percent fully vaccinated would complicate the interpretation of the variable as vaccine refusal, since it would exclude those that were willing to get the first dose but did not get the second before our time cutoff. The PVR data used to compute UP is sourced from Georgetown Universitys U.S. COVID-19 Vaccination Tracking website, which primarily relies on CDC data, supplemented with vaccination data from local health departments where CDC data is incomplete16. Vaccination data is not available for 69 counties in Alaska, Nebraska, Georgia, and Virginia, so these counties were excluded from the analysis. Additionally, since the Johnson & Johnson (J&J) vaccine only requires one dose to be fully vaccinated, the PVR excludes individuals who got the J&J vaccine. However, only 3.3% of vaccinations administered were J&J as of December 15, 202125, so the exclusion of J&J vaccinations should have minimal impact on our conclusions.

While our response variable measures lack of vaccine uptake, not hesitancy directly, we believe that this work will still provide insight on factors correlated with vaccine hesitancy. Previous work found that COVID-19 vaccine uptake is strongly correlated with vaccine hesitancy, as measured by survey data6. Further, vaccine uptake rates reflect real world vaccination behavior at the population level, in contrast to vaccine hesitancy surveys which are available for a subset of locations around the U.S. and suffer from sampling biases. In addition, we selected the time cutoff of December 15, 2021 in order to minimize the impact of non-hesitancy factors on uptake, such as vaccine eligibility and accessibility. Therefore, our response variable serves as a reasonable proxy for vaccine hesitancy, and we think it is the best choice possible based on the data that is currently available.

All demographic and socioeconomic variables are sourced from publicly available datasets at the county level, from the U.S. Census Bureaus 2020 Decennial Census17 and U.S. Department of Agriculture (USDA) Economic Research Service18. The percentage of Black people and the percentage of Latinx people represent the self-identified proportion of those races in each county. Postsecondary education percentage is measured as the percentage of adults with educational attainment more advanced than completing high school. The uninsured percentage is the percentage of people who reported not having health insurance. Additional metrics include median age, average number of vehicles per household and the median household income.

The political affiliation variable, defined as the percentage of voters who chose Donald Trump as their presidential candidate during the 2020 presidential election, is sourced from the MIT Election Data and Science Lab19. Previous work has found this data to be associated with vaccine hesitancy9,10. Compared with other political indicators, such as other election results, voter registration data, or public opinion polls, the presidential election has the highest voter turnout and the most policy influence. We hereby adopt this metric as a proxy of the political affiliation. It is referred as Republican presidential vote percentage (%) in the study.

In efforts to explore whether a county that experienced more burden from COVID-19 may be more willing to adopt preventative measures such as vaccination, we incorporate a variable to capture a county's historical COVID-19 infection rate. Specifically, to measure historical COVID-19 burden, we use the cumulative number of COVID-19 cases per 100,000 people as of December 15, 2021 from the Johns Hopkins University Center for Systems Science and Engineering (CSSE) COVID-19 GitHub20. In order to remove outliers, values that were more than 4 standard deviations above the mean were excluded.

Since MMR vaccination coverage is an indicator of vaccine acceptance before COVID-19, we hypothesize that higher (pre-pandemic) MMR vaccine uptake rates may be associated with higher COVID-19 vaccine coverage. Previous work has shown a strong association between MMR coverage and vaccine hesitancy26,27. In most U.S. states, the MMR vaccine is required for children to attend public school, making MMR coverage a strong indicator of anti-vaccination behavior. While this data reflects the results of parents making vaccination decisions for their children, in contrast to measuring COVID-19 uptake among mostly adult populations, the decision to forgo mandatory childhood vaccinations is indicative of strong hesitancy that we hypothesize may transfer to other vaccines. To test this hypothesis, we incorporate a variable in this analysis that is based on the MMR vaccination coverage rates of children in kindergarten in 2019, data that we gathered in a previous study21.

A set of variables intended to capture the potential role of information consumption on vaccine choice includes four television viewership rating variables and a Twitter misinformation variable. The county level viewership ratings (RTG) % for four major channels, namely FNC (Fox News Channel), CNN (Cable News Network), MSNBC (Microsoft National Broadcasting Company), and local news, are sourced from Nielsen Media, where RTG is measured by the estimated percentage of households tuned to a specific viewing source, e.g., news channel. The four viewership variables were computed as the average of the monthly viewership ratings for each channel from February to November 2021. January 2021 data were excluded due to anomalies caused by the January 6th U.S. Capital Attack. Nielsen data is not available for several counties in Virginia and Alaska and all counties in Hawaii, so these 72 counties are excluded from the analysis. The analysis also excludes outliers, defined as those counties with cable viewership values that are more than 4 standard deviations away from the mean. Additionally, within the model each of the cable TV viewership variables was standardized to a mean of 0 and standard deviation of 1, to provide a more interpretable understanding of the relative position of each countys rating.

Another information consumption variable included in the model is the Twitter misinformation variable. This variable is intended to capture the prevalence of COVID-19 vaccine misinformation in circulation on Twitter during a time that likely influenced behavior during the study period. The variable is based on a previous study by Pierri et al., who provided a variable that is representative of the percent of COVID-19 vaccine-related tweets that contain links to low credibility sources at the county level15,23. This variable has some limitations, as it is based on only the set of Twitter accounts that can be geolocated. To ensure a large enough sample size for a reliable estimate, counties with less than 50 geolocated accounts are not included, which results in a data set that includes 904 counties.After excludingoutliers, defined as values that are more than 4 standard deviations from the mean, we have 855 counties. An analogous data set is also available with a minimum of 10 and 100 geolocated accounts, but we opted to use the cutoff of 50 to balance having a more representative sample size of accounts per county with the number of counties we can include in our analysis. Due to the limited number of counties that this data is available for, a separate sub-analysis is conducted that includes this variable (Fig.2).

Various land-use variables are sourced from the U.S. Census Bureau, namely the population size and the number of residents in rural or urban areas for each county28. These variables are used to cluster counties for the sub-analyses, which are further described in the methods section. For the cluster-based analysis we categorize counties into mutually exclusive sets based on (1) population quartiles and (2) a binary rural or urban classification. For the binary classification, a county is classified as rural if the majority of the population is designated to live in areas classified as rural and otherwise classified as urban.

We use a Generalized Additive Model (GAM) to explore the relationship between each county's unvaccinated percentage and the aforementioned variables. GAMs provide a balance between model complexity and interpretability, and critically, they can reflect the relative importance of different features29,30. Specifically, GAMs model the response variable as the sum of unknown smooth functions of covariates, and unlike Generalized Linear Models (GLMs), GAMs offer the capability to model nonlinear relationships between variables. For example, a linear regression model may show an overall positive correlation between an input variable and the response variable, but using a GAM on the same data may reveal a more nuanced relationship, like a strong positive trend in some ranges of the input variable and a weak negative trend elsewhere. Due to the complex nature of relationships between our variables and vaccination uptake, GAMs are a more appropriate choice than linear models, since they can capture both linear and nonlinear relationships.

The proposed GAM is fitted to the unvaccinated percentage as the response variable, which is assumed to have a Gaussian distribution, and a log link. REML (restricted maximum likelihood) is used to estimate smoothing parameters, which returns relatively reliable and stable results. Specifically, the model in our primary analysis has the following form:

$${Y}_{i}sim Gaussian(mu )$$

$${text{log}}left(mu right)sim {s}_{1}left(cumulative ,COVID-19, case, rateright)+ {s}_{2}left(percentage, of ,Black, peopleright)+ {s}_{3}left(percentage, of ,Hispanic, peopleright)+ {s}_{4}left(postsecondary, education, percentageright)+ {s}_{5}(median ,household ,income) + {s}_{6}(median ,age) + {s}_{7}(vehicles, per, household) + {s}_{8}(uninsured, percentage) + {s}_{9}(MMR, coverage) + {s}_{10}(std(FNC, viewership) + {s}_{11}(std(CNN ,viewership)) + {s}_{12}(std(MSNBC, viewership) + {s}_{13}(std(Local, News, viewership) + {s}_{14}(Republican ,presidential ,vote ,percentage)$$

where Yi denotes the unvaccinated percentage for each county i. The model is a sum of smooth functions ({s}_{i}), and each smooth function consists of a number of basis functions (k). Sensitivity analysis that varies the number of basis functions was conducted. A value of k=3 for each smooth function was found to provide the optimal balance between preventing both underfitting and overfitting of the model and maximizing interpretability of the results. Additionally, the GAM model is weighted to prevent the highly imbalanced county population distribution from skewing the results. The weight is computed by normalizing each countys population by the average county population, taking a log transformation to adjust for the skewness. The weight implemented in the primary analysis is defined as:

$$weigh{t}_{i}=frac{{text{log}}(po{p}_{i})}{mean({sum }_{i}{text{log}}(po{p}_{i}))},$$

where i is the county index. The primary model is run for 2885 counties (reduced from the full set of counties due to missing data and data quality issues referenced previously).

As noted previously, the Twitter misinformation variable, ({s}_{15}(Twitter ,misinformation)), is only available for 855 counties, and is therefore run as a separate model using the same general function and weights as the primary model, but with the additional determinant included.

In addition to the primary model presented above, we conduct sub-analyses to determine how the relationship between unvaccinated percentage and its associated factors varies across urban versus rural counties. In the U.S., vaccination uptake was substantially lower in rural areas31. Multiple studies have examined the reasons for this discrepancy. Some factors associated with lower vaccination in rural areas were captured in our study, including lower educational attainment, voting for Trump in the 2020 election, and lower insurance coverage32. However, other relevant factors could not be incorporated in our study, including that rural residents have a lower perceived risk of COVID-19, higher vaccine hesitancy, and are less likely to adopt covid risk mitigating behaviors32,33. In order to better understand the influence of different factors in rural versus urban counties, we complete two cluster-based sub-analyses: one clustered by rural versus urban counties and another clustered into four quartiles based on population size, as described below. Due to the difficulty of neatly separating counties into urban or rural, we provide the population size cluster analysis to confirm that our urbanrural analysis is accurately capturing differences between urban and rural counites, which broadly aligns with higher versus lower county populations.

Land-use cluster-based analysis: Counties are clustered into two groups based on their primary land use pattern, namely as urban or rural counties. Two independent weighted GAM models are run, one for each group. The rural model includes 1,835 counties, and the urban model includes 1,050 counties.

Population cluster-based analysis: Counties are grouped into quantiles based on their population size. Four independent GAM models are generated, one for each distinct quantile. The respective models contain 664, 721, 739, and 761 counties ranging from the smallest to largest population size groups. GAMs are implemented without weights for each group in this sub-analysis, because the weighting is based on population size.

We evaluate the goodness-of-fit by conducting a diagnostic analysis for each model and sub-model. These evaluations include the Q-Q plots, histograms of residuals, mapping of residual values versus predicted values, and mapping of response against fitted values. The diagnostic analysis outcomes for the primary model are presented in the Supplementary Material (Fig. S2). The concurvity in the primary model is also measured to ensure pairwise values remain below 0.8 and avoid cases in which one variable is a smooth function of another. The outcomes of the diagnostic analysis demonstrated consistency in fit and performance across all models.

Read more:

A population level study on the determinants of COVID-19 vaccination rates at the U.S. county level | Scientific Reports - Nature.com

Calculation of excess deaths

The number of excess deaths in each period and age-sex-geography stratum is calculated as the difference between the observed and expected number of deaths:

Where E-hat[i,t] is the estimated number of excess deaths, d[i,t] is the observed number of deaths and d-hat[i,t] is the expected number of deaths in age-sex-geography stratum i in period t.

The estimated total number of excess deaths in each period is obtained by summing estimated excess deaths across age groups, sexes, and geographies:

This "bottom-up" approach ensures additivity throughout the aggregation structure. For example:

estimated excess deaths by age group for males in a particular UK country will sum to the total estimated excess deaths across all age groups for males in that UK country

estimated excess deaths for males and females in a particular UK country will sum to the total estimated excess deaths for both sexes combined in that UK country

estimated excess deaths in individual UK countries (England and Wales combined, Scotland, and Northern Ireland), including deaths among non-residents, will sum to the total estimated excess deaths for the UK

Temporal additivity between monthly and annual estimates of excess deaths is also achieved by summing the estimated excess deaths obtained from the monthly model to derive annual totals. However, weekly estimates will not necessarily sum to annual estimates, as weeks may straddle calendar years at the beginning and end of each year.

It will always be the case that the number of excess deaths in a period is an estimate rather than a known value, because the number of expected deaths is a counterfactual quantity that must be estimated from observed data using statistical techniques. To reflect this uncertainty inherent in expected and excess deaths estimates, 95% confidence intervals are constructed around the excess deaths estimates using the following formula:

Where E-hat[i,t] is the estimated expected deaths in age-sex-geography stratum i in period t, and SE(E-hat[i,t]) is the standard error of the estimate, which is the square root of the variance of the estimate, V(E-hat[i,t]). See our Uncertainty and how we measure it methodology for more information on confidence intervals and standard errors.

The number of excess deaths is estimated as the difference between the observed and expected number of deaths, so the variance of the estimated excess deaths is a combination of the variances of both these quantities. However, the observed number of deaths is a known quantity rather than an estimate, so it has no variance. Therefore:

Where d-hat[i,t] is the expected number of deaths in age-sex-geography stratum i in period t and V(d-hat[i,t]) is its variance, approximated through the Delta method.

The overall variance of the expected total number of deaths across age groups, sexes and geographies in each period can be found by summing the stratum-specific variances within periods:

The population denominators used to calculate mortality rates in each period and age-sex-geography stratum are derived from mid-year population estimates. These population estimates are not timely enough to feed into contemporary estimates of excess deaths. For example, estimates relating to mid-2022 were not published until August 2023 for Northern Ireland, on the Northern Ireland Statistics and Research Agency (NISRA) website, and November 2023 for England and Wales in our 2021-based National population projections bulletin. They have not yet been published for Scotland.

In the future, the Dynamic Population Model and resulting admin-based population estimates may provide more timely estimates (see our Admin-based population estimates: local authorities in England and Wales article). For the time being, the mid-year population estimates are extrapolated with population projections in each age-sex-geography stratum. Historical estimates of excess deaths will be revised whenever population projections for a given year are replaced by the mid-year population estimate.

National population projections are typically updated once every two years but subnational projections (needed for population denominators in the English regions) are only updated once every four years. These are published several months after the corresponding national update. For example, our 2021-based National population projections bulletin was published in January 2024, and before this our 2020-based National population projections bulletin was published in January 2022.

However, our latest available Subnational population projections bulletin (2018-based) was published in March 2020. Therefore, contemporary population sizes for the English regions are obtained by applying the regional proportions from the latest mid-year population estimates to the latest available national population projections for England. This ensures that the population denominators used for calculating mortality rates across the English regions sum to the national population denominator for England.

Population estimates and projections relate to the estimated population size at the mid-point of each year, but population denominators are needed on weekly and monthly bases for excess deaths calculations. Therefore, weekly and monthly population estimates are linearly interpolated between the mid-year estimates.

The pandemic saw a large increase in death registrations, particularly in certain weeks and months that coincided with "waves" of infection (for example, when new COVID-19 variants became widespread in the population). To avoid these periods affecting estimates of expected deaths in subsequent periods, they are removed from the dataset when the model is fitted so that they do not contribute to the mortality baseline. This means that estimates of excess deaths in subsequent periods relate to the additional deaths registered in the period, over and above what would be expected from previous periods had they not been extraordinarily affected by the pandemic.

We define periods extraordinarily affected by the direct mortality impacts of the pandemic as being those where COVID-19 was given as the underlying cause of death for at least 15% of all deaths registered in the period across the UK. This threshold gives the greatest coherence between the weekly and monthly data in terms of periods excluded from the model fitting. These periods are April and May 2020, and November 2020 to February 2021 for monthly data; they are Weeks 14 to 22 of 2020, and Week 45 of 2020 to Week 8 of 2021 for weekly data.

The annual calendar on which we report our weekly mortality statistics usually comprises 52 seven-day weeks and is 364 days in length. By contrast, the Gregorian calendar year (used by most countries across the world) is 365 days long for non-leap years and 366 days long for leap years. This means that the reporting calendar slips out of alignment with the Gregorian calendar by one or two days each year. To avoid this misalignment becoming too severe, there is international agreement that a "Week 53" should periodically be added to the reporting calendar.

Week 53 occurs infrequently (it was last added to the mortality calendar in 2020, and before that in 2015), so it is not practical to estimate a separate seasonal term for it when fitting models to five years of data. Instead, any instances of Week 53 are re-labelled as Week 52 when fitting models and obtaining expected numbers of deaths. This assumes that the mortality rate in a typical Week 53 is similar to a typical Week 52.

In the future, it is anticipated that we will publish estimates of excess deaths in each of the four UK countries as well as the total excess deaths in the UK as a whole. National Records of Scotland (NRS) and NISRA will also separately publish estimates of excess deaths for Scotland and Northern Ireland, respectively, using the same methodology as the Office for National Statistics (ONS). This will ensure consistent and comparable estimates across all parts of the UK.

For consistency with the death registrations data we publish and the devolved administrations, the following models are fitted to estimate excess deaths:

deaths registered in England or Wales, including those for non-residents

deaths registered in Scotland, including those for non-residents

deaths registered in Northern Ireland, including those for non-residents

deaths registered in England or Wales among residents of England

deaths registered in England or Wales among residents of Wales

The total number of estimated excess deaths across the UK is then derived by summing the outputs from the first three models listed. The fourth model listed includes English region of residence as an explanatory variable.

In practice, 10 models are fitted to obtain estimates of excess deaths: five for weekly data and five for monthly data. In addition, five models are fitted to the annual data to obtain standard errors and confidence intervals around the annualised estimates (monthly excess deaths estimates can be summed within years to obtain annual estimates, but this is not possible for the standard errors because of the existence of correlation between successive monthly estimates, which is generally the case with any time series data). To obtain the variance of the annualised estimate, we assume that its coefficient of variation is the same as that of the estimate from the model fitted to annual data.

The models fitted to annual data include age group, sex, English region (only in the model for deaths registered in England or Wales among residents of England), a trend component and the number of weekdays in the year.

In our current approach to estimating excess deaths in England and Wales, and that of the devolved administrations of Scotland and Northern Ireland, the expected (baseline) number of deaths is estimated as the average number of deaths registered in a recent five-year period. In contrast, our new methodology is based on age-specific mortality rates rather than death counts, so trends in population size and age structure are accounted for. Furthermore, the five-year average mortality rate is adjusted for a trend, so historical changes in population mortality rates are also accounted for.

Before the pandemic, the five-year period used in the current methodology was the five years preceding the current year. For example, the expected number of deaths in 2019 was estimated as the average number of deaths registered from 2014 to 2018 (inclusive). Weekly and monthly expected deaths were estimated as the average number of deaths registered in the same week or month over the past five years. For example, the expected number of deaths in Week 1 of 2019 was estimated as the average number of deaths registered in Week 1 from 2014 to 2018 (inclusive).

The expected number of deaths in 2021 was estimated as the average of deaths registered from 2015 to 2019 rather than 2016 to 2020, to avoid the pandemic distorting the excess deaths calculation. The expected number of deaths in 2022 was estimated as the average of deaths registered in 2016, 2017, 2018, 2019 and 2021.

In contrast, individual weeks and months, rather than whole years, that were substantially affected by the immediate mortality impact of the pandemic are removed from the expected deaths calculation under the new methodology.

Other improvements brought about by the change in methodology include:

use of a statistical model means that multiple demographic, trend, seasonal and calendar effects can be included simultaneously in the estimation of expected deaths, and confidence intervals can readily be obtained

a "bottom-up" approach to aggregation means that estimates of excess deaths are additive across age groups, sexes, and high-level geographies, and between months and years

having a common methodology for all four UK countries means that estimates of excess deaths are consistent and comparable across all parts of the UK, and the new methodology is largely coherent (though not identical) to that used by the Office for Health Improvement and Disparities (OHID) to estimate excess deaths in English local authorities.

Excerpt from:

Estimating excess deaths in the UK, methodology changes: February 2024 - Office for National Statistics

Dr. Anthony Fauci visited University of Illinois Chicago on Tuesday to discuss his decades-long career in medicine and public service.

Marie Lynn Miranda, UICs chancellor, spoke with Fauci about his work battling two major public health crises the HIV and AIDS epidemic and the COVID-19 pandemic the need for more robust local public health systems to better manage future outbreaks and restoring trust in health care professionals and scientists.

The 83-year-old retired in 2022 after leading the National Institute of Allergy and Infectious Diseases for nearly 40 years. He served as an adviser on domestic and global health issues to seven U.S. presidents.

About 2,100 people attended the chat at UICs Isadore and Sadie Dorin Forum, including former Mayor Lori Lightfoot, who did not speak. At three separate points, pro-Palestine demonstrators with the organization Behind Enemy Lines interrupted Fauci and Miranda and were removed from the auditorium.

Pro-Palestinian demonstrators disrupted Dr. Anthony Faucis talk on Tuesday at UIC.

During the chat, Fauci discussed the countrys failures in responding and managing the COVID-19 pandemic. He pointed out that before the outbreak, the Johns Hopkins School of Public Health ranked the U.S. as overwhelmingly the best country for pandemic preparedness.

But at the end of the fourth year of the pandemic, we had 1.7 million deaths and the outbreak isn't even over yet, Fauci said. We are now in our fifth year of this outbreak, and that is more deaths per capita than virtually any other country in the world, including low- and middle-income countries with maybe one exception.

The countrys diminishing public health system was one of the reasons the response to the virus was weakened, especially given that most local public health departments are critically underfunded, Fauci said.

So when you heard us always talking about how we can control [COVID-19] by identification, isolation and contact tracing, all of that takes place at the local level, Fauci said. And if you don't have local public health infrastructure, no matter how devoted and committed the people are, if they don't have the resources, it's not going to happen.

Dr. Anthony Fauci, who is the former director of the National Institute of Allergy and Infectious Diseases at the National Institutes of Health and former chief medical advisor to Pres. Joe Biden, speaks to University of Illinois Chicago Chancellor Marie Lynn Miranda during Chair Chats at the UIC Isadore and Sadie Dorin Forum, Tuesday, Feb. 20, 2024.

At the federal level, responding to COVID-19 was complicated by the ever-changing virus, Fauci said.

When you're dealing with the moving target, then you have to make decisions based on the information, the data and the evidence that you have, Fauci said. So what we knew in January of 2020, was very different from so many standpoints than what we knew in July of 2020 and in April of 2022.

COVID-19 wasnt the first outbreak Fauci handled. During the early 1980s, he was on the forefront of responding to the growing HIV and AIDS epidemic. He started working for the National Institutes of Health in 1981 after finishing fellowships in infectious disease and immunology.

Nobody else wanted to get involved in treating and learning about the disease, Fauci said, and some didnt think it was even worth addressing.

Fauci said his undergrad degree in classical studies made him a more empathetic doctor, enabling him to see patients such as those with AIDS as people outside of their infections.

I just felt this compelling empathy towards these young gay men who were being stigmatized, not only for being gay, but stigmatized because they had a disease that was a mysterious disease that was likely sexually transmitted, Fauci said.

I felt an almost fundamental ethical responsibility to say I'm going to change the direction of my career and start studying this disease that didn't have a name.

Dr. Anthony Fauci, who is the former director of the National Institute of Allergy and Infectious Diseases at the National Institutes of Health and former chief medical advisor to Pres. Joe Biden, speaks during Chair Chats at the University of Illinois Chicago Isadore and Sadie Dorin Forum, Tuesday, Feb. 20, 2024.

Read the original here:

Dr. Anthony Fauci discusses his career, reflects on pandemic response at UIC forum - Chicago Sun-Times

Representational image

Amidst the battle against the COVID-19 pandemic, vaccines emerged as a beacon of hope, offering humanity a vital tool against the deadly virus. However, recent research has introduced a concerning twist to this narrative.

Since the onset of the pandemic, approximately 13.5 billion COVID-19 vaccines have been administered worldwide, marking a significant milestone in the fight against the virus. Notably, around 71% of the global population has received at least one dose of a COVID vaccine.

Despite the widespread vaccination efforts, a recent peer-reviewed study has now sparked health concerns among this substantial portion of the global population. Its findings have linked COVID vaccines from prominent companies such as Pfizer, Moderna and AstraZeneca to rare occurrences of heart, brain and blood disorders.

Researchers from the Global Vaccine Data Network, a research arm of the World Health Organisation (WHO), conducted the largest COVID vaccine study to date. It analysed expected versus observed rates of 13 medical conditions considered adverse events of special interest in a study population of 99 million vaccinated individuals across eight countries.

Here are some key associations the study found:

In spite of these findings, experts emphasise that the benefits of vaccination far outweigh the risks. For instance, the likelihood of experiencing neurological events or heart inflammation is significantly higher after contracting COVID-19 than after receiving a COVID-19 vaccine.

The odds of all of these adverse events is still much, much higher when infected with SARS-CoV-2 (COVID-19), so getting vaccinated is still by far the safer choice, CEO of biotechnology company Centivax Jacob Glanville, who is not involved in the study, told Forbes.

In navigating the complex landscape of risks and benefits, the overarching message remains clear: vaccination remains an indispensable tool in the fight against COVID-19, offering a pathway toward overcoming the disease's pervasive grip on global health and well-being.

The study was published in the journal Vaccine.

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Research Links COVID Vaccines With Increased Risk of Heart, Brain and Blood Disorders - The Weather Channel

Today in JAMA, researchers show that US renters who were served eviction notices in the first 2 years of the COVID-19 pandemic had a high proportion of excess deaths.

Investigators from Princeton University and Rutgers University-Newark analyzed linked eviction and death records from January 2020 to August 2021, comparing them with projected death rates estimated from comparable records in 2010 to 2016. Two comparison groups were made up of all people living in the study area and a subsample of those living in high-poverty, high-eviction housing tracts who didn't themselves face eviction.

The median age of the threatened renters was 36 years, 62.5% were women, 57.6% were Black, and their median annual household income was $38,000, with 25.9% living below the poverty line.

Renters who pay a large proportion of their income for housing face health consequences from having to prioritize rent over health-related expenses like food and healthcare,psychosocial distress, and reduced ability to address immediate and long-term health needs, the researchers said.

"The COVID-19 pandemic worsened this crisis by contributing to large-scale job and wage loss, especially for Black and Hispanic renters who already experienced the highest rates of housing instability owing to an ongoing history of discriminatory housing policies and practices," they wrote.

A total of 282,000 renters received an eviction notice in 2020 and 2021, during which time their observed death rate was 106% higher (238.6 deaths per 100,000 person-months) than the expected rate (116.5 per 100,000)or more than double.

The COVID-19 pandemic worsened this crisis by contributing to large-scale job and wage loss, especially for Black and Hispanic renters who already experienced the highest rates of housing instability.

In contrast, the observed death rate among similar renters not served notices was 25% higher (142.8 deaths per 100,000 person-months) than the expected rate (114.6 per 100,000). In the general population living in the study area, the expected death rate was 83.5 per 100,000 person-months, compared with an observed death rate of 91.6 per 100,000, or 9% higher than expected.

During the study period, eviction filings were 44.7% lower than expected, and Black women faced the highest proportion of eviction filings (38.7%), despite making up only 11.5% of renters.

"The pandemic roughly doubled mortality rates across all ages for threatened renters, translating to large absolute excess mortality for older age groups," the authors noted. "The filed-against population is much younger than the general population; a large proportion of filings during the pandemic targeted renters aged 25 to 40 years, ages when background mortality tends to be much lower."

Eviction filings pose a risk of displacement even if they do not result in formal eviction, by pushing out tenants who know that the odds of receiving a favorable judgment in housing court are against them, the researchers said.

"Combined with associated fines and fees, filing alone can push renters into even more precarious and overcrowded housing, increasing risk of exposure to COVID-19," they wrote. "While fewer renters faced the threat of eviction during the pandemic, owing to programs such as eviction moratoria and the Emergency Rental Assistance program, threatened renters were at far greater risk of death."

The results, the investigators said, show that those who face eviction are an especially at-risk population. "Our results highlight the importance of monitoring health outcomes among marginalized populations that can't currently be disaggregated in national health statistics," they concluded. "Our results also underscore the need for policymakers and researchers to take into consideration access to safe and stable housing when designing health interventions."

Read this article:

Those getting eviction notices during COVID pandemic at greater risk for death, study finds - University of Minnesota Twin Cities

AUSTIN (KXAN) A new study published this month found possible links between the COVID-19 vaccine and health conditions.

The research is the largest study of its kind linking the shot to slight increases in neurological, blood and heart-related conditions.

But some experts stress getting COVID far outweighs the risks of getting vaccinated.

Its important to understand that last year COVID was the leading cause of death amongst children from a disease caused by an infection. So its critically important that children get vaccinated against COVID, said Dr. Michelle Fiscus a Chief Medical Officer leading a CDC-funded project to improve COVID-19 vaccination rates in children.

However, this latest study may fuel the debate of risks and benefits of vaccines.

I understand there has been a lot of and misinformation on social media and sometimes even from people who look like me who are physicians, said Dr. Fiscus.

But as a mom and as a pediatrician, I will tell you that these vaccines that we give children are the most tested of any medications that we use for children. COVID vaccines are the most highly tested amongst any vaccines that weve ever had. And its critically important that we protect our children from the suffering that these diseases can cause.

For the latest news, weather, sports, and streaming video, head to KXAN Austin.

Read more:

New research may fuel debate of risks and benefits of COVID-19 vaccine - Yahoo News

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Texas Attorney General Ken Paxton is suing Pfizer, alleging that the pharmaceutical giant lied about the effectiveness of its COVID-19 vaccine. LiveNOW's Carel Lajara spoke about the lawsuit with Infectious disease expert Dr. Peter Chin-Hong.

The largest COVID vaccine study to date has identified some risks associated with the shot.

Researchers from the Global Vaccine Data Network (GVDN) in New Zealand analyzed 99 million people who received COVID vaccinations across eight countries.

They monitored for increases in 13 different medical conditions in the period after people received a COVID vaccine.

The study, which was published in the journal Vaccine last week, found that the vaccine was linked to a slight increase in neurological, blood and heart-related medical conditions, according to a press release from GVDN.

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People who received certain types of mRNA vaccines were found to have a higher risk of myocarditis, which is inflammation of the heart muscle.

Some viral-vector vaccines were linked to a higher risk of blood clots in the brain, as well as an increased likelihood of Guillain-Barre syndrome, a neurological disorder in which the immune system attacks the nerves.

Other potential risks included inflammation of part of the spinal cord after viral vector vaccines, and inflammation and swelling in the brain and spinal cord after viral vector and mRNA vaccines, the press release stated.

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"The size of the population in this study increased the possibility of identifying rare potential vaccine safety signals," lead author Kristna Faksov of the Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark, said in the release.

"Single sites or regions are unlikely to have a large enough population to detect very rare signals."

File: A pharmacist, prepares to give a COVID-19 vaccine in Cypress, Texas, Sept. 20, 2023. (Melissa Phillip/Houston Chronicle via Getty Images)

Dr. Marc Siegel, clinical professor of medicine at NYU Langone Medical Center and a Fox News medical contributor, was not involved in the research but commented on the findings.

"The massive study and review of the data reveals some rare association of the MRNA vaccines and myocarditis, especially after the second shot, as well as an association between the Oxford Astra Zeneca adenovirus vector vaccines and Guillain Barre syndrome," he told Fox News Digital.

"But these risks are rare," he added, "and other studies show that the vaccine decreases the risk of myocarditis from COVID itself dramatically."

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Siegel noted that all vaccines have side effects.

"It always comes down to a risk/benefit analysis of what you are more afraid of the vaccine's side effects or the virus itself, which can have long-term side effects in terms of brain fog, fatigue, cough and also heart issues," he said.

"Denying or exaggerating a vaccine's side effects is not good science nor is underestimating the risks of the virus, especially in high-risk groups," Siegel added.

"It comes down to a risk/benefit analysis of what you are more afraid of the vaccine's side effects or the virus itself."

The key is for doctors and their patients to carefully weigh the risks and benefits, the doctor emphasized.

"This study does not really change anything; it just provides much further evidence of what we already know," he said.

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Health officials say the number of Americans receiving flu shots and COVID-19 treatments are down (Credit: FOX News/News Edge)

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Dr. Jacob Glanville, CEO of Centivax, a San Francisco biotechnology company, also reacted to the studys findings.

"This study is confirming in a much larger cohort what has been previously identified in the original studies during the pandemic myocarditis and pericarditis as a rare side effect of mRNA vaccines and clots as a rare side effect of the viral vectored vaccines," he told Fox News Digital.

"The odds of all of these adverse events are still much, much higher when infected with SARS-CoV-2 (COVID-19), so getting vaccinated is still by far the safer choice."

This study was part of a more widespread research initiative, the Global COVID Vaccine Safety (GCoVS) Project.

The project is supported by Centers for Disease Control and Prevention (CDC), a component of the U.S. Department of Health and Human Services (HHS).

More than 80% of the U.S. population has received at least one dose of the COVID vaccine, per the CDC.

Fox News Digital reached out to Pfizer and Moderna, makers of mRNA COVID vaccines, for comment.

LINK: FOR UPDATES ON THIS STORY, CLICK OVER TO FOXNEWS.COM

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Largest-ever COVID vaccine study links shot to small increase in heart, brain conditions - FOX 5 New York

RSV on the rise in Montana, as weekly COVID-19 and influenza counts decrease

by NBC Montana Staff

A young child with RSV is treated at Community Medical Center in Missoula, Montana. Photo: Community Medical Center

MISSOULA, Mont.

Data from the Montana Department of Health and Human Services show a decline in weekly COVID-19 and influenza cases and hospitalizations, even as weekly RSV numbers increase.

The state reports 454 positive COVID cases, 10 hospitalizations, and no deaths for the week ended Feb. 10. Hospitalizations peaked at 56 on Nov. 18. Total weekly cases peaked at 936 the week of Dec. 2. Weekly numbers of COVID-19 related deaths peaked the week of Oct. 14 at 9. So far this season there have been 12,594 cases of COVID-19, 686 hospitalizations, and 77 deaths.

The state reports 1,160 influenza cases for the week ended Feb. 10, with 22 hospitalizations and zero deaths. Weekly flu case numbers peaked at 1,990 cases the week ended Dec. 20. Flu hospitalizations also peaked that week at 124. Weekly influenza deaths peaked the week ended Jan. 6, with eight deaths reported. So far this season 36 influenza-related deaths have been recorded in Montana, with 750 hospitalizations and 15,232 cases.

RSV has been on the rise in recent weeks. For the week ended Feb. 10 there were 69 positive RSV cases recorded, up from 55 the week before.

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RSV on the rise in Montana, as weekly COVID-19 and influenza counts decrease - NBC Montana

The numbers of microorganisms with the potential to harm humans is very large, but the resources for research and development (R&D) are limited.

To address this problem, the World Health Organization (WHO) convened a panel of scientists and public health experts in December 2015 to prioritise the top five to 10 emerging pathogens likely to cause severe outbreaks and the greatest public health risk in the near future, because of their epidemic potential and/or for which few or no medical countermeasures exist.

The initial list included Crimean Congo haemorrhagic fever, Ebola and Marburg virus, Lassa fever, Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS) coronavirus diseases, Nipah (which first occurred in Malaysia) and Rift Valley fever.

It was intended that these diseases will provide the basis for work on the WHO Blueprint for R&D preparedness to help control potential future outbreaks.

The list was updated in 2018 and 2022.

The current list includes Covid-19, Crimean-Congo haemorrhagic fever, Ebola and Marburg virus diseases, Lassa fever, MERS-CoV, SARS, Nipah and henipaviral diseases, Rift Valley fever, Zika and Disease X.

Disease X

Disease X was the name given by WHO to a currently unknown pathogen that could emerge in the future and cause a serious international epidemic or pandemic.

It was included in WHOs Blueprint list in February 2018 of diseases for which investment in R&D should be an international priority to enable early preparedness for the unknown Disease X.

Disease X does not exist currently.

But the concept of Disease X is a very real and growing threat to human health for which the world must prepare better to respond to.

The most recent Disease X was the SARS-CoV-2 virus which caused Covid-19.

Because the world was unprepared to defend itself against it, Covid-19s rapid spread caused a pandemic, which was reported to have affected more than 700 million people and caused about seven million deaths globally.Malaysia reported more than 5.2 million Covid-19 cases, the highest among Asean countries. Public Health Malaysia Facebook page

Malaysia was not spared with more than 5.2 million cases and 37,000 deaths reported, the highest death rate per capita among Asean nations and maternal mortality of 68 per 100,000 live births in 2021, a number last seen in the mid-1980s.

Scientists at Imperial College Londons MRC Centre for Global Infectious Disease estimated that Covid-19 vaccines saved an estimated 20 million lives globally between December 8, 2020 and December 8, 2021.

In Malaysia, the Institute for Clinical Research reported that vaccines saved an estimated 16,000 to 17,000 lives after half the population got fully vaccinated in September 2021.

Covid-19 is still around, albeit in a milder form, with waves still occurring.

When will Disease X occur?

No one can predict when Disease X will occur.

However, what is certain is that a future Disease X is out there and will, at some point in time, emerge and begin to spread to cause a disease outbreak.

In the past three decades, there have been increasingly frequent outbreaks of pathogens capable of causing severe disease and death to humans.

In the 21st century, there have been outbreaks of SARS-CoV-1, MERS, Zika, Ebola and other new and re-emerging viral diseases such as Chikungunya.

While it is likely that the next Disease X could be a novel disease, there is also the possibility of already known pathogens mutating and/or re-emerging in a more virulent form and then spreading into epidemics or pandemics.

Novel diseases emerge all the time, often making the leap from animals like bats to humans.

Scientists believe that the next Disease X is highly likely to be a virus that will emerge from about 25 families of viruses that have already shown their capability to cause human disease.

Climate change, urbanisation and globalisation are increasing the likelihood and frequency of infectious diseases outbreaks globally.

Scientists from Princeton University, United States analysed a global dataset of large epidemics spanning four centuries.Bats are primary vectors for virus transmission from animals to humans. TNS

They reported: The rate of occurrence of epidemics varies widely in time, but the probability distribution of epidemic intensity assumes a constant form with a slowly decaying algebraic tail, implying that the probability of extreme epidemics decreases slowly with epidemic intensity.

Together with recent estimates of increasing rates of disease emergence from animal reservoirs associated with environmental change, this finding suggests a high probability of observing pandemics similar to Covid-19 (probability of experiencing it in ones lifetime currently about 38%), which may double in coming decades.

In laymans language, the researchers found that the chance of a pandemic, with a similar impact to Covid-19, is about one in 50 in any year.

This means the lifetime probability of anyone reading this experiencing a pandemic similar to Covid-19 is about 38%, which may double in coming decades.

Preparedness critical

Disease X has been described as a hidden but inevitable creeping danger.

Although Covid-19 has had a devastating impact on the world, it has gone, perhaps disappeared, into the background and many healthcare systems have gone back to status quo.

Many governments and politicians have viewed the weak recovering economies from Covid-19 as reason to delay funding for preparedness for another epidemic/pandemic, as a result of which timely effective measures will be in short supply when Disease X comes along.

Whilst there may be anxiety about Disease X and its inevitability, it does not mean that the world is destined to relive the devastating impacts of Covid-19.

Steps can be taken to stop Disease X and reduce its spread and damage by proper and pre-emptive preparation this will be addressed in a subsequent article.

Dr Milton Lum is a past president of the Federation of Private Medical Practitioners Associations and the Malaysian Medical Association. For more information, email starhealth@thestar.com.my. The views expressed do not represent that of organisations that the writer is associated with. The information provided is for educational and communication purposes only, and it should not be construed as personal medical advice. Information published in this article is not intended to replace, supplant or augment a consultation with a health professional regarding the readers own medical care. The Star disclaims all responsibility for any losses, damage to property or personal injury suffered directly or indirectly from reliance on such information.

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Time to get ready to fight Disease X - The Star Online

By Deidre McPhillips, CNN

(CNN) Housing instability carries deadly risks, and these vulnerabilities were exacerbated for many during the Covid-19 pandemic.

The mortality rate among renters who faced eviction was twice as high as expected during the first two years of the pandemic, according to a study published on Tuesday in JAMA. The general population also experienced excess mortality during this time, but the risk started higher for renters and rose exponentially for those threatened with eviction.

From January 2020 through August 2021, the risk of death for renters facing eviction was 2.6 times greater than it was in the general population, the study found. During the baseline period of 2010 to 2016, the mortality rate was 1.4 times higher for renters facing eviction than it was for the general population.

The pandemic has really highlighted how housing is so critical to health and public health, said Nick Graetz, a researcher at Princeton Universitys Eviction Lab and lead author of the study. That all became really clear and salient during a pandemic where there was this massive infectious disease risk. But this deep connection between housing and health, its not new. Its not going to disappear if Covid numbers come down.

The new study is the third in a series of work from a collaboration between the Eviction Lab and the US Census Bureau, with aims to fill gaps in understanding about who faces eviction in the United States and what the impacts are.

Work published in October showed that about 7.6 million people are threatened with eviction each year, and risks are highest for households with children and Black renters. Another study from December explored the risk between rising rent costs and mortality risk.

The latest study is the first to focus on a pandemic timeframe, and the findings focus on eviction filing trends in 36 court systems that cover about 400 counties. While the findings for the new study are not nationally representative, they do show stark connection.

Data limitations prevented the researchers from identifying causes of death among renters facing eviction, but experts point to the negative effects of chronic stress as a likely driver.

Eviction filings may represent a general state of financial strain among renters, which in turn leads to chronic stress increasing health risks and mortality, Jack Tsai, a professor at UTHealth Houston School of Public Health and director of research for the US Department of Veterans Affairs National Center on Homelessness among Veterans, wrote in a related editorial.

Chronic stress is a known risk factor for many of the leading causes of death in the US during the pandemic, including heart disease, cancer and stroke, he wrote.

Many of these leading causes of death are associated with older age, but the average renter threatened with eviction during the pandemic was just 36 years old, according to the new study.

Overwhelmingly, we should think of this as premature death in terms of what a healthy life expectancy should be in the United States, richest country in the world, Graetz said. Older folks are experiencing really intense housing insecurity, but we know that most (eviction) filings occur among folks around age 25 to 50, so thats where the bulk of this excess mortality burden is going to come from.

For advocates working to help individuals facing eviction, the toll that the process has on physical and mental health is readily apparent.

A lot of times people talk about how that stress is impacting them. Fatigue is definitely something that sets in for a lot of people, theyre not sleeping well. And I have had multiple clients, as well, who have autoimmune diseases that report flare ups due to the stress, said Katie Derrick, a community health worker at Jesse Tree, a social services organization focused on preventing eviction and homelessness in Boise, Idaho.

The relationship between housing insecurity and health is cyclical, Derrick said. Health issues are the second most common reason people say they apply for services at Jesse Tree.

An unexpected medical bill or missing work to care for a sick child can cause a person to fall behind on rent, she said. A negative housing situation can then lead to more negative health impacts, which then require more resources.

In addition to the chronic stress of housing instability, Tsai also notes in his editorial that eviction filings can lead renters to move or be displaced to inferior housing environments that may increase their health risks.

Crowded, unsanitary, or otherwise unsafe living conditions can increase the risk of transmission of diseases like COVID-19 and others, such as tuberculosis, he wrote. Low-quality housing can also affect a persons health through exposure to toxins such as lead and asbestos, noise pollution and harsh weather elements.

Eviction filings were down 45% during the first two years of the pandemic, according to the new study.

But experts warn that deadly health risks may grow, as many public assistance programs that were in effect in the early years of the pandemic such as eviction moratoriums are ended.

As we move out of maybe the most intense period of Covid prevalence and mortality, we return to a normal that was already untenable, and its been getting worse, Graetz said. Rent burdens are as high as theyve ever been, and eviction filings are now back and surpassing historical averages.

In the last quarter of 2023, more than two out of five people who applied for housing support services from Jesse Tree in Idaho had been to the emergency department, half of whom had been more than once over the course of three months, Derrick said.

Accessing the ED for things that could be addressed with a primary care physician costs a lot more, she said. Its eye-opening how inaccessible care is for folks.

Even for those who do have access to care, some patients may not feel comfortable raising concerns about housing with their doctor. When patients talk about struggling to afford their medications, thats often a clue that more is going on, said Dr. StevenFurr, a family physician in Alabama and president of the American Academy of Family Physicians.

As physicians, were focused on treating the whole patient and we realize that the social world in which they live is so important to their health care, said Furr, who was not involved in the new study. We realize that the medications and diagnoses we give them are about 20% of the issue, and where they live and where they work is about 80%. So were constantly asking them what kind of environment theyre in, whats going on with their situation.

Some Medicare programs recently started paying providers to screen patients with a social determinants of health assessment, he said, which may encourage even more focus on these issues.

On Maslows hierarchy of needs, shelter is grouped with air, water and food as one of the bare necessities for human survival.

Its one of the basic foundations that you need to build health and build employment, Tsai told CNN. If you dont have that, its really challenging to start moving on to addressing the other social determinants of health.

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Mortality surged for renters facing eviction during the pandemic, study finds - ABC17News.com