Beyond the sine wave: unveiling climate-driven baseline mortality cycles

The relationship between temperature and mortality is well-established, with higher mortality rates occurring in moderate climates during winter. Studies on COVID-19 and influenza-related excess deaths often assume a sine-like wave pattern for baseline mortality. This study aims to assess the accuracy of this approximation in capturing the observed mortality pattern and explore its linkage with climate. Weekly mortality data from European regions (2000-2019) were modeled using the seasonal-trend decomposition procedure based on Loess. Cycles were grouped into clusters, and underlying trends were extracted using principal component analysis. Generalized linear models assuming a sine-like pattern were used to test predictive value. Cluster analysis divided the regional cycles approximately into continental and temperate climate regions, further subdivided into oceanic and Mediterranean. While the continental region exhibited a sine-like mortality pattern, it displayed modest deviations that compounded further south. The period of elevated winter mortality became shorter but more intense, while decreased summer mortality became more pronounced yet delayed. This study improves weekly estimations of excess mortality models by providing enhanced baselines. The deviation from the sine-like approximation mirrors the idealized outbreak pattern from epidemiological models with sharper surges and more gradual declines. The results point to winter infections, impacted by acquired immunity and weather conditions, as the primary drivers of fluctuations in mortality. In warmer regions, there is an apparent shift toward a lower number of overall infections within a compressed time span.

Exploring the paradoxical nature of cold temperature mortality in Europe

While low winter temperatures are associated with increased mortality, this phenomenon has been suggested to be most severe in regions with seemingly mild winters. The study aimed to establish a temperature-based formula that could elucidate the previously ambiguous regional differences in vulnerability to low temperature. European weekly mortality data (2000-2019) were matched with meteorological data to determine for each region vulnerability to temperature decrease and the optimal temperature with lowest mortality. Regression models were developed to generalize and explain these findings considering regional temperature characteristics. Optimal temperature could be predicted based on local average summer temperature (R2 = 85.6%). Regional vulnerability to temperature decrease could be explained by combination of winter and summer temperatures (R2 = 86.1%). Regions with warm winters and cold summers showed the highest vulnerability to decrease of temperature during winter. Contrary to theories about economic disparities Eastern Europe exhibited resistance comparable to Scandinavia. The southern edges of Europe demonstrated serious low temperature vulnerability to decreased temperatures, even if temperature was relatively high around 20 °C. This suggests that the observed connection primarily reflects the modulation of the length of respiratory virus infection seasons by climate conditions, counterbalanced by varying levels of acquired immunity and the presence of heatwaves eliminating the most frail individuals. Thus, relatively low vulnerability and a flat mortality cycle in countries with harsh climates paradoxically imply the presence of threats throughout the whole year.

From respiratory diseases to nervous system disorders: Unraveling the certified causes of influenza-associated deaths in Poland from 2000 to 2019

Background

This study aims to accurately estimate influenza-associated deaths in Poland and their certified cause of death, due to significant discrepancies between official numbers and expected impact.

Methods

Excess influenza-associated mortality in Poland from 2000 to 2019 was calculated using Seasonal-Trend Decomposition Procedure based on LOESS (STL), which can detect non-linear trends and non-sinusoidal cycles. Excess mortality was then used as an explanatory variable in a model predicting monthly fluctuations of officially recorded causes of death from 2010 to 2019.

Results

A total of 142,000 conservative estimates of influenza-associated deaths were identified, representing 1.86% of overall mortality. Only 0.61% of influenza-associated deaths were officially recorded as influenza. Nearly half of certified influenza deaths were attributed to the seasonal baseline mortality, potentially doubling estimated impact based solely on influenza peaks. Influenza-associated deaths were frequently recorded as respiratory diseases (24.36%), with majority attributed to underlying conditions such as cardiovascular diseases (45.31%), cancer (9.06%), or diabetes (2.66%). Influenza-associated deaths were more commonly certified as nervous system diseases (1.84%) or mental disorders (1.04%), rather than influenza itself. There was a noticeable impact of influenza on secondary infections, such as meningococcal and gastrointestinal infections.

Conclusion

These findings highlight the importance of improved estimation for informing public health policy decisions.

Seasonal Pattern in Gestational Diabetes Mellitus in Poland: A Retrospective Cohort Study

The existing literature does not address the question of the seasonal impact on pregnancy in Central-Eastern Europe; therefore, this study was designed to investigate the seasonal variation in gestational diabetes mellitus (GDM) based on a recent Polish sample. The data of 30,205 newborns from singleton pregnancies and their mothers, including the date and gestational age of birth, neonatal sex and weight, maternal age and parity, mode of delivery, ethnicity, and a detailed list of comorbidities (including GDM), were retrospectively analysed. The prevalence of GDM was significantly (p < 0.0001) lower in spring (14.71%) than in the other seasons (16.78%). A higher incidence of GDM was observed for mothers who underwent an oral glucose tolerance test from June to August compared to those who were tested from December to February (17.34% vs. 14.75%, p < 0.0001). Similarly, there were significant differences between seasons with higher and lower insolation. The regression analysis revealed that seasonal patterns were significantly associated with the prevalence of GDM. In conclusion, this large retrospective cohort study demonstrated seasonal changes in GDM risk. The observed seasonal patterns may equally refer to mothers of babies born at term and prematurely. Further research concerning GDM risk and other seasonal and gender associations is warranted.

Unmasking the COVID-19 pandemic prevention gains: excess mortality reversal in 2022

OBJECTIVES

The purpose of this study was to assess the long-term effectiveness of COVID-19 pandemic prevention measures in saving lives after European governments began to lift restrictions.

STUDY DESIGN

Excess mortality interrupted time series.

METHODS

Country-level weekly data on deaths were fitted to the Poisson mixed linear model to estimate excess deaths. Based on this estimate, the percentage of excess deaths above the baseline during the pandemic (week 11 in 2020 to week 15 in 2022) (when public health interventions were in place) and during the post-pandemic period (week 16 in 2022 to week 52 in 2022) were calculated. These results were fitted to the linear regression model to determine any potential relationship between mortality during these two periods.

RESULTS

The model used in this study had high predictive value (adjusted R2 = 59.4%). Mortality during the endemic (post-pandemic) period alone increased by 7.2% (95% confidence interval [CI]: 5.7, 8.6) above baseline, while each percentage increase in mortality during the pandemic corresponded to a 0.357% reduction (95% CI: 0.243, 0.471) in mortality during the post-pandemic period.

CONCLUSIONS

The most successful countries in terms of protective measures also experienced the highest mortality rates after restrictions were lifted. The model used in this study clearly shows a measure of bidirectional mortality displacement that is sufficiently clear to mask any impact of long COVID on overall mortality. Results from this study also seriously impact previous cost-benefit analyses of pandemic prevention measures, since, according to the current model, 12.2% (95% CI: 8.3, 16.1) of the gains achieved in pandemic containment were lost after restrictions were lifted.

Cluster Donation: How Future Healthcare Professionals Bound Certain Types of Tissues and Biomedical Research and How It Affects Their Willingness to Donate

Although biomedical research requires cooperation with a large number of donors, its success also depends on the input of healthcare professionals who play a crucial role in promoting biomedical research and influencing an individual's decision to donate one's biospecimens that are left over after a medical procedure. This work was aimed at investigating the correlation between medical and healthcare students' willingness to donate a biospecimen, the type of tissues to be donated, and the type of biomedical research to be conducted. A population survey among medical and healthcare students enrolled at the Poznan University of Medical Sciences was conducted on their attitudes toward the donation of human biological material for research purposes. A total of 1500 students responded and completed the survey. The questionnaire used multiple-choice closed-ended questions designed to explore medical and healthcare students' preferences for sharing particular types of tissues and donating to a particular type of biomedical research. It asked three questions: 1. Which type of tissue would people be willing to donate for research purposes? 2. Which organs would they be willing to donate after death? 3. What type of research would they be willing to donate to? While future healthcare professionals' beliefs regarding certain types of tissues and research can influence their willingness to donate for research purposes, many students tend to think about the body and biomedical research in terms of clusters. Consequently, their willingness to donate certain tissues for a particular type of research can affect their decision to donate other tissues. Our data suggest that cluster thinking with regard to donation can be a predictor of people's readiness to participate in the collection and management of biospecimens.

To vaccinate or to isolate? Establishing which intervention leads to measurable mortality reduction during the COVID-19 Delta wave in Poland

Background

During the Delta variant COVID-19 wave in Poland there were serious regional differences in vaccination rates and discrepancies in the enforcement of pandemic preventive measures, which allowed us to assess the relative effectiveness of the policies implemented.

Methods

Creating a model that would predict mortality based on vaccination rates among the most vulnerable groups and the timing of the wave peak enabled us to calculate to what extent flattening the curve reduced mortality. Subsequently, a model was created to assess which preventive measures delayed the peak of infection waves. Combining those two models allowed us to estimate the relative effectiveness of those measures.

Results

Flattening the infection curve worked: according to our model, each week of postponing the peak of the wave reduced excess deaths by 1.79%. Saving a single life during the Delta wave required one of the following: either the vaccination of 57 high-risk people, or 1,258 low-risk people to build herd immunity, or the isolation of 334 infected individuals for a cumulative period of 10.1 years, or finally quarantining 782 contacts for a cumulative period of 19.3 years.

Conclusions

Except for the most disciplined societies, vaccination of high-risk individuals followed by vaccinating low-risk groups should have been the top priority instead of relying on isolation and quarantine measures which can incur disproportionately higher social costs. Our study demonstrates that even in a country with uniform policies, implementation outcomes varied, highlighting the importance of fine-tuning policies to regional specificity.

Are We Facing a Tsunami of Vaccine Hesitancy or Outdated Pandemic Policy in Times of Omicron? Analyzing Changes of COVID-19 Vaccination Trends in Poland

In this study, we analyzed Polish COVID-19 vaccination data until January 2023 from the European Centre for Disease Prevention and Control to understand individual decision making during the milder Omicron wave. Our findings show a general decline in subsequent vaccine uptake. As the number of government-provided doses increased, completion rates among certain low-risk groups dropped to less than 1%. Elderly individuals, especially those aged 70–79, showed greater adherence but also exhibited decreased interest in subsequent boosters. Healthcare workers exhibited a dramatic shift in their attitude, disregarding the recommended schedule. The overwhelming majority opted out of receiving the second boosters, while the remaining individuals adjusted their timing based on infection trends or the availability of updated boosters. Two factors positively influenced vaccination decisions: societal influence and the availability of updated boosters. Lower-risk individuals were more likely to postpone vaccination until updated boosters were available. Our findings highlight that while Polish policy aligns with international guidelines, it fails to garner significant adherence from the Polish population. Previous studies have shown that vaccinating low-risk groups resulted in more sick days due to adverse events following immunization than the days gained by preventing infection. Consequently, we advocate for the official abandonment of this policy, as its practical abandonment has already taken place, and persisting in pretending otherwise only serves to erode public trust. Therefore, we propose a shift toward treating COVID-19-like influenza with vaccination for vulnerable individuals and those who have close contact with them before the season.

Are We Facing a Tsunami of Vaccine Hesitancy or Outdated Pandemic Policy in Times of Omicron? Analyzing Changes of COVID-19 Vaccination Trends in Poland

In this study, we analyzed Polish COVID-19 vaccination data until January 2023 from the European Centre for Disease Prevention and Control to understand individual decision making during the milder Omicron wave. Our findings show a general decline in subsequent vaccine uptake. As the number of government-provided doses increased, completion rates among certain low-risk groups dropped to less than 1%. Elderly individuals, especially those aged 70-79, showed greater adherence but also exhibited decreased interest in subsequent boosters. Healthcare workers exhibited a dramatic shift in their attitude, disregarding the recommended schedule. The overwhelming majority opted out of receiving the second boosters, while the remaining individuals adjusted their timing based on infection trends or the availability of updated boosters. Two factors positively influenced vaccination decisions: societal influence and the availability of updated boosters. Lower-risk individuals were more likely to postpone vaccination until updated boosters were available. Our findings highlight that while Polish policy aligns with international guidelines, it fails to garner significant adherence from the Polish population. Previous studies have shown that vaccinating low-risk groups resulted in more sick days due to adverse events following immunization than the days gained by preventing infection. Consequently, we advocate for the official abandonment of this policy, as its practical abandonment has already taken place, and persisting in pretending otherwise only serves to erode public trust. Therefore, we propose a shift toward treating COVID-19-like influenza with vaccination for vulnerable individuals and those who have close contact with them before the season.

Better Late Than Never: Predictors of Delayed COVID-19 Vaccine Uptake in Poland

In this study, regression models were created to explain the increase of COVID-19 vaccination rates in 378 Polish sub-regions. In order to trace the factors that could explain the willingness to delay vaccination, vaccination rates were compared for age groups of 20 years and more for 30 June 2020 and 31 January 2021. Initially high vaccination rates, rather than leading to the gradual exhaustion of the pool of those wishing to get vaccinated, were a very good predictor of the share of the remainder willing to do so, which increased the divergence between sub-regions in nominal vaccination rates. Support for Eurosceptic and anti-establishment parties was a strong predictor of persistent vaccine hesitancy. Ideological divergence from the mainstream appeared to reinforce vaccine hesitancy, and this relationship remained highly relevant even when controlling for possible time or spatial lag. Markers of social inclusion and social capital—voter turnout and employment rate—remained statistically significant even when controlling for time lag, thus implying clear relevance of trust in the public message. The share of the population with higher education remained a highly relevant factor as well, though in the 20–39 age bracket it predicted a higher vaccination rate, while in all older brackets it was a negative predictor—this implies that those people had already made up their minds. Delaying vaccination seems predominantly explainable by political views, as well as social exclusion and the historical specificity of sub-regions. On a regional level, there was actually a paradoxical Spearmans Rho correlation (0.641) between the share of population refusing mandatory vaccination for kids and the percentage of people receiving a COVID-19 vaccine, which further undermines the idea that overall observed vaccine hesitancy was in any meaningful way affected by anti-vaccine movements.

Better Late Than Never: Predictors of Delayed COVID-19 Vaccine Uptake in Poland

In this study, regression models were created to explain the increase of COVID-19 vaccination rates in 378 Polish sub-regions. In order to trace the factors that could explain the willingness to delay vaccination, vaccination rates were compared for age groups of 20 years and more for 30 June 2020 and 31 January 2021. Initially high vaccination rates, rather than leading to the gradual exhaustion of the pool of those wishing to get vaccinated, were a very good predictor of the share of the remainder willing to do so, which increased the divergence between sub-regions in nominal vaccination rates. Support for Eurosceptic and anti-establishment parties was a strong predictor of persistent vaccine hesitancy. Ideological divergence from the mainstream appeared to reinforce vaccine hesitancy, and this relationship remained highly relevant even when controlling for possible time or spatial lag. Markers of social inclusion and social capital—voter turnout and employment rate—remained statistically significant even when controlling for time lag, thus implying clear relevance of trust in the public message. The share of the population with higher education remained a highly relevant factor as well, though in the 20–39 age bracket it predicted a higher vaccination rate, while in all older brackets it was a negative predictor—this implies that those people had already made up their minds. Delaying vaccination seems predominantly explainable by political views, as well as social exclusion and the historical specificity of sub-regions. On a regional level, there was actually a paradoxical Spearmans Rho correlation (0.641) between the share of population refusing mandatory vaccination for kids and the percentage of people receiving a COVID-19 vaccine, which further undermines the idea that overall observed vaccine hesitancy was in any meaningful way affected by anti-vaccine movements.

Underestimation in Reporting Excess COVID-19 Death Data in Poland during the First Three Pandemic Waves

The issue whether official Polish COVID-19 death statistics correctly reflect the actual number of deaths is a contentious issue in public discourse and an important policy-wise question in Poland although it has not been the subject of thorough research so far. There had been clearly elevated excess mortality—5100 (death rate of 2.3 per 10,000) during the first wave, 77,500 (21.0 per 10,000) during the second one, and 48,900 (13.5 per 10,000) in the third. This study finds that during the second and the third pandemic wave, our data on excess mortality will match very well the somewhat belatedly officially reported COVID-19 deaths if we assume that only 60% of cases were officially detected. Based on principal component analysis of death timing, except for the age bracket below 40, where COVID-19 deaths calculated on the basis of our model explain 55% of excess mortality, for the remaining age groups, combined COVID-19 deaths explain 95% of excess mortality. Based on the share of excess mortality attributable to COVID-19 during the second wave, this infection in Poland caused the death of 73,300 people and not of 37,600 as officially reported. The third wave caused 46,200 deaths instead of the reported 34,700. The first wave was, indeed, as officially reported, very mild, and the number of excess deaths was too low to be used to calculate COVID-19 deaths directly. However, assuming that the detection rate remained comparable to the average in subsequent waves, we can set the number of deaths at 3500 instead of the reported 2100.