Military conflicts may be ongoing and encompass multiple medical facilities. This study investigated the impact of a military conflict (“Protective Edge” PE) on emergency department (ED) function in a tertiary medical center.

Visits to the ED during PE (July-August 2014) were compared with ED visits during July-August 2013 and 2015 with regard to admission rates, waiting times and 30-d mortality. Odds ratios (ORs) adjusted for confounders were used for the multivariable regression models.

There were 32,343 visits during PE and 74,279 visits during the comparison periods. A 13% decrease in the daily number of visits was noted. During PE, longer waiting times were found, on average 0.25 h longer, controlling for confounders. The difference in waiting times was greater in medicine and surgery. Admission rates were on average 10% higher during PE military conflict, controlling for confounders. This difference decreased to 7% controlling for the daily number of visits. Thirty-day mortality was significantly increased during PE (OR = 1.42; 95% CI: 1.18-1.70). ORs for mortality during PE were significantly higher in medicine (OR = 1.45; 95% CI: 1.15-1.81) and pediatrics (OR = 4.40; 95% CI: 1.33-14.5).

During an ongoing military conflict, waiting times, admission rates, and mortality were statistically significantly increased.

This paper explores compounding challenges for older coastal populations due to accelerated sea level rise at the nexus of physical hazard exposure and place-based socioeconomic and health considerations.

This study applies geospatial analysis to assess the spatial distribution of older adults (age 65+) and their socioeconomic characteristics in Miami-Dade County in Florida. Next, it uses logistic regression to evaluate the socioeconomic determinants of block groups with 20% or more of residents age 65 and older at 3 feet of sea level rise compared to the other block groups in Miami-Dade.

The results show that this study area has an older population clustered in flood-prone locations along the shoreline. The block groups with more than 20% of older adults and sea level rise risk have higher homeownership and vacancy rates, a higher percentage of homes constructed before 1980, and more older individuals who live alone.

This study identifies place-based compounding factors undermining the ability of older residents to adequately cope with accelerated sea level rise flooding in coastal urban locations. Namely, owning an older home in a declining neighborhood and living alone can trap older individuals in place and increase their flood risk.

The aim of this study was to evaluate how relationships between guardians and domestic animals were established and their possible effects during the COVID-19 pandemic period in Brazil.

Data were collected by completing an online questionnaire. Throughout the national territory, 2002 people completed the questionnaire, while respecting social distancing from January 20, 2021, to March 20, 2021. The inclusion and exclusion criteria were ‘to own pets’ or ‘those who had them during the period of quarantine’ in Brazil. The data obtained were treated qualitatively through discourse analysis and content analysis, while the quantitative data were tabulated by the questionnaire application platform itself.

The results showed that 97% of the participants were affected by the coronavirus pandemic in Brazil and that of these, 95.5% stated that their pet was important to overcome and bear bad feelings during the period of social isolation. It was possible to observe through the participants’ reports through the online questionnaire that the relationships with their pets were deepened during the pandemic period, and that these had an important role in overcoming bad emotions caused by social distancing.

It was concluded that due to these relationships, behaviors such as anxiety and sadness were reduced and the pets themselves also showed an increase in affective behaviors in relation to their guardians.

To describe the health-care resources implemented during the Italian Formula 1 Grand Prix (F1GP) and to calculate the patient presentation rate (PPR) based on both real data and a prediction model.

Observational and descriptive study conducted from September 9 to September 11, 2022, during the Italian F1GP hosted in Monza (Italy). Maurer’s formula was applied to decide the number and type of health resources to be allocated. Patient presentation rate (PPR) was computed based on real data (PPR_real) and based on the Arbon formula (PPR_est).

Of 336,000 attendees, n = 263 requested medical assistance with most of them receiving treatment at the advanced medical post, and n = 16 needing transport to the hospital. The PPR_real was 51 for Friday, 78 for Saturday, 134 for Sunday, and 263 when considering the whole event as a single event. The PPR_est resulted in 85 for Friday, 93 for Saturday, 97 for Sunday, and 221 for the total population.

A careful organization of health-care resources could mitigate the impact of the Italian F1GP on local hospital facilities. The Arbon formula is an acceptable model to predict and estimate the number of patients requesting medical assistance, but further investigation needs to be conducted to implement the model and tailor it to broader categories of MGE.

The rapid onset of coronavirus disease 2019 (COVID-19) created a complex virtual collective consciousness. Misinformation and polarization were hallmarks of the pandemic in the United States, highlighting the importance of studying public opinion online. Humans express their thoughts and feelings more openly than ever before on social media; co-occurrence of multiple data sources have become valuable for monitoring and understanding public sentimental preparedness and response to an event within our society.

In this study, Twitter and Google Trends data were used as the co-occurrence data for the understanding of the dynamics of sentiment and interest during the COVID-19 pandemic in the United States from January 2020 to September 2021. Developmental trajectory analysis of Twitter sentiment was conducted using corpus linguistic techniques and word cloud mapping to reveal 8 positive and negative sentiments and emotions. Machine learning algorithms were used to implement the opinion mining how Twitter sentiment was related to Google Trends interest with historical COVID-19 public health data.

The sentiment analysis went beyond polarity to detect specific feelings and emotions during the pandemic.

The discoveries on the behaviors of emotions at each stage of the pandemic were presented from the emotion detection when associated with the historical COVID-19 data and Google Trends data.

The effects of named weather storms on the rates of penetrating trauma is poorly understood with only case reports of single events currently guiding public health policy. This study examines whether tropical storms and hurricanes contribute to trauma services and volume.

This was a cross-sectional review of tropical storms/hurricanes affecting New Orleans, Louisiana, during hurricane seasons (June 1–November 30) from 2010–2021, and their association with the rate of penetrating trauma. Authors sought to determine how penetrating trauma rates changed during hurricane seasons and associate them with demographic variables.

There were 5531 penetrating injuries, with 412 (7.4%) occurring during landfall and 554 (10.0%) in the aftermath. Black/African Americans were the most affected. There was an increase in the rate of penetrating events during landfall (3.4 events/day) and aftermath (3.5 events/day) compared to the baseline (2.8 events/day) (P = < 0.001). Using multivariate analysis, wind speed was positively related to firearm injury, whereas the rainfall total was inversely related to firearm violence rates during landfall and aftermath periods. Self-harm was positively related to distance from the trauma center.

Cities at risk for named weather storms may face increasing gun violence in the landfall and aftermath periods. Black/African Americans are most affected, worsening existing disparities. Self-harm may also increase following these weather events.

This study examined various factors influencing protective actions among persons with disabilities exposed to the December 10–11, 2021, tornado outbreak in the US Midwest and Southeastern regions.

Survey administration occurred 5 months following the tornado outbreak and included a total of 209 persons with disabilities who lived in one of the counties impacted by tornado warnings. Structural equation modeling was used to examine the direct and indirect effects of hypothesized predictors impacting protective behavioral actions.

Results found that persons with disabilities who had access to more tornado warning sources increased their protective actions, and tornado risk perception further mediated the relationship between warning information sources and protective actions. In addition, results found that persons with disabilities who encountered more situational barriers in their physical and social environment were found to have a decrease in protective action, and having more situational resources was found to contribute to the ability to take protective action.

The current study’s results highlight the need for policies and practices that provide additional physical and social resources for persons with disabilities to seek protection during tornado threats.

To study the respiratory patterns and the hemodynamic variations related to postural changes in inpatients with coronavirus disease (COVID-19).

This report is a prospective study in a cohort of inpatients admitted with COVID-19. We recruited 10 patients admitted to the hospital with moderate or severe COVID-19 who showed improvement in oxygen saturation with prone positioning. We performed cardiorespiratory polygraphy and hemodynamic evaluations by thoracic electrical bioimpedance.

We observed a median minimum oxygen saturation of 85.00% (IQR: 7.00) in the supine position versus 91.00% (IQR: 8.00) (P = 0.173) in the prone position. The airflow restriction in the supine position was 2.70% (IQR: 6.55) versus 1.55% (IQR: 2.80) (P = 0.383) in the prone position. A total of 36.4% of patients were classified as having a normo-hemodynamic state in the supine position, whereas 54.5% were classified in this group in the prone position (P = 0.668). A decrease in vascular resistance was observed in the prone position (18.2% of vasoconstriction) compared to the supine position (36.4% of vasoconstriction) (P = 0.871).

This brief report describes the effects of prone positioning on respiratory and hemodynamic variables in 10 patients with moderate or severe COVID-19.

Vaccine acceptance and trust in vaccines pose a complex process affected by many factors. The present study was conducted to determine coronavirus disease 2019 (COVID-19) vaccine acceptance, trust in vaccines, anxiety levels, and related factors in Turkish society.

The data of this cross-sectional and descriptive-correlational study were collected with the snowball method by using an online questionnaire throughout Turkey. The study was conducted between March 15 and April 3, 2021, with 3148 participants from 7 regions and 81 cities in Turkey.

It was found that the participants accepted the vaccine at 72.8%, and the trust rate in the vaccine was 66.0%. It was also found that women, single participants, those who had immune system diseases, and with COVID-19 had higher Coronavirus Anxiety Scale scores at significant levels. According to Logistic Regression Analysis, gender, age, trust in the vaccine, perception of risk levels regarding COVID-19, and coronavirus anxiety levels are factors affecting the intentions of participants to accept/reject the vaccine. It was determined that male participants were more likely to accept the coronavirus vaccine (P = 0.028). It was found that health-care employees had higher trust in the coronavirus vaccine (P = 0.006) and acceptance rates (P = 0.010) at significant levels compared with the general population.

The COVID-19 vaccine acceptance rate in Turkish society was found to be high, and the level of trust in vaccines and anxiety levels were above the moderate level.

Vaccination is crucial to fighting the coronavirus disease (COVID-19) pandemic. A large body of literature investigates the effect of the initiation of the COVID-19 vaccination in case numbers in Turkey, including the resistance and willingness to taking the vaccine. The effect of early relaxation in the Turkish public with the initiation of vaccination on new daily cases is unknown.

This study performs an event study analysis to explore the pre-relaxation effect of vaccination on the Turkish public by using daily data of new cases, stringency index, and residential mobility. Two events are comparatively defined as the vaccination of the health personnel (Event 1) and the citizens age 65 and over (Event 2). The initial dates of these events are January 13 and February 12, 2021, respectively. The length of the estimation window is determined as 14 days for the 2 events. To represent only the early stages of the vaccination, the study period ends on April 12, 2021. Thus, whereas the event window of Event 1 includes 90 observations, Event 2 covers 60 observations.

While average values of residential mobility, stringency index, and daily numbers of cases are 15.36, 71.03, and 11 978.93 in the estimation window for Event 1, these averages are 8.89, 70.88, and 17 303.20 in the event window. For Event 2, the same average values are 9.14, 69.38, and 7 664.93 in the estimation window and 8.25, 71.12, and 22 319.10 in the event window. When 14-day abnormal growth rates of the daily number of cases for Event 1 and Event 2 are compared, it is observed that Event 1 has negative growth rates initially and reaches a 7.59% growth at most. On the other hand, Event 2 starts with a 1.11% growth rate, and having a steady increase, it reaches a 23.70% growth in the last 14 days of the study period.

The preliminary result shows that, despite taking more strict governmental measures, while residential mobility decreases, the daily number of COVID-19 cases increases in the early stages of vaccination compared to short pre-periods of it. This indicates that the initiation of vaccination leads to early behavioral relaxation in public. Moreover, the effect of Event 2 on the case numbers is more significant and immediate, compared to that of Event 1, which may be linked to the characteristic of the Turkish culture being more sensitive to the older adult population.

The aim of this study was to identify and prioritize strategies for strengthening public health system resilience for pandemics, disasters, and other emergencies using a scorecard approach.

The United Nations Public Health System Resilience Scorecard (Scorecard) was applied across 5 workshops in Slovenia, Turkey, and the United States of America. The workshops focused on participants reviewing and discussing 23 questions/indicators. A Likert type scale was used for scoring with zero being the lowest and 5 the highest. The workshop scores were analyzed and discussed by participants to prioritize areas of need and develop resilience strategies. Data from all workshops were aggregated, analyzed, and interpreted to develop priorities representative of participating locations.

Eight themes emerged representing the need for better integration of public health and disaster management systems. These include: assessing community disease burden; embedding long-term recovery groups in emergency systems; exploring mental health care needs; examining ecosystem risks; evaluating reserve funds; identifying what crisis communication strategies worked well; providing non-medical services; and reviewing resilience of existing facilities, alternate care sites, and institutions.

The Scorecard is an effective tool for establishing baseline resilience and prioritizing actions. The strategies identified reflect areas in most need for investment to improve public health system resilience.

North Dakota (ND) had the highest coronavirus disease 2019 (COVID-19) case and mortality rate in the United States for nearly 2 mo. This study aims to compare 3 metrics ND used to guide public health action across its 53 counties.

Daily COVID-19 case and death totals in North Dakota were evaluated using data from the COVID-tracker website provided by the North Department of Health (NDDoH). It was reported as: active cases per 10,000, tests administered per 10,000, and test positivity rate (the North Dakota health metric). The COVID-19 Response press conferences provided data for the Governor’s metric. The Harvard model used daily new cases per 100,000. A chi-squared test was used to compare differences in these 3 metrics on July 1, August 26, September 23, and November 13, 2020.

On July 1, no significant difference between the metrics was found. By September 23, Harvard’s health metric indicated critical risk while ND’s health metric was moderate risk, and the Governor’s metric was still low risk.

ND’s and the Governor’s metric underrepresented the risk of the COVID-19 outbreak in North Dakota. The Harvard metric reflected North Dakota’s increasing risk; it should be considered as a national standard in future pandemics.

Model-based predictors could guide policy-makers to effectively control spread of infectious disease; proactive models could reduce risk of disease as it progresses in vulnerable communities.