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BackgroundSoldiers in modern armies perform tasks that are increasingly technologically dependent. Training them to obtain necessary technological skills is both complex and expensive. Personnel attrition is costly and can affect military readiness. The purpose of this study was to examine the utility of pre-induction tests as a predictor of attrition in the first year of infantry training.Methods303 infantry recruits participated in the study. Before beginning military service their health profile was determined and they were given a Quality Group score, which is determined by psycho-technical tests, a personal interview and the quality of their education. Recruits were screened shortly following induction using a battery of tests including questionnaires, anthropometrics, functional movement screening (FMS), upper and lower quarter Y balance tests, dynamic tests, and followed by orthopaedists and their unit doctors for orthopaedic injuries and problems during the first year of training.Results165/303 (54.5%) recruits were diagnosed with injury or pain during the course of their first year of training. 46 recruits (15.1%) did not complete their first year of service as combatants and 18 (5.9%) were discharged from service. On multivariable analysis for attrition, protective factors were higher Quality Group scores (OR 0.78, CI 0.69-0.89) and recruits diagnosed with orthopaedic injuries or musculoskeletal pain (OR 0.21 CI 0.09-0.50). Pain in the balance test performed at the beginning of training was a risk factor (OR 3.39, CI 1.47-7.79). These factors together are only responsible for 15.4% of infantry attrition according to partial eta squared analysis. ConclusionsThe three variables found by multivariable analysis to be associated with infantry attrition in this study together are responsible for 15.4% of the attrition. Measuring these variables would seem to be most valuable in armies in which the number of candidates for a specific infantry unit greatly exceeds the number of positions. The IDF approach of trying to keep attriters within the Army in non-combatant roles and not discharging them from service is a way to manage the problem of infantry attrition. Trial RegistrationProspectively registered on clinicaltrials.gov, registration number NCT02091713

Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.

In December 2019, an infectious pandemic outbreak occurred in the city of Wuhan in the Province of Hubei, China. The pathogen was identified as a novel coronavirus - COVID-19. This virus belongs to a family of viruses that cause Severe Acute Respiratory Syndrome, known as SARS-COV. The disease is characterized by a high mortality rate among adults aged 60 years or above, particularly those with chronic comorbidities. Databases available on the web provide updated, real-time data on the incidence and mortality rates ascribed to the COVID-19 pandemic in various countries. However, to draw accurate epidemiologic conclusions, demographic data (population density, age distribution, and urbanization level), as well as clinical data (number of screening tests and number of days since the first detected disease case in the country) must be taken into consideration. Informed use of these data affords reliable epidemiologic analysis. For example, a comparison of COVID-19 case fatality rates between Germany and Iran – two countries similar in population size and urbanization level – reveals that the mortality rate in Iran is significantly higher than that of Germany, while the active morbidity burden is much higher in Germany. This may seem surprising, given that Germany’s population is considerably older than that of Iran and four times as dense. It may be surmised that the quality and availability of health services in Germany are superior to those in Iran, offering a higher number of screening tests and more effective clinical treatment. Another important factor affecting morbidity spread is the timing of a lockdown policy implementation. For example, a comparison between China and the USA – two countries with similar land area and median age – reveals that in spite of the fact that in China population density is about 4.25 times higher than in the USA, morbidity rate is considerably lower than in the USA. Two factors can be considered responsible for this lower rate: lower urbanization and an earlier lockdown policy compared with the USA.

Introduction The common practice in the Israel Defense Force is that all exertional heat related injuries victims undergo a heat tolerance test (HTT) as a part of the “return to duty” process. The purpose of this study was to develop a quantitative, supportive physiological index for the assessment of the HTT based on the understanding that heat strain level should combine the thermal and cardiovascular strains. Materials and methods The HTT results of 104 individuals with a history of heat injuries were retrospectively analyzed after randomly divided into two groups (an analysis group and a validation group). Rectal temperature and heart rate were monitored continuously during the test. Using the ratio between those two variables we constructed the TCR (Thermal-Circulatory Ratio) index and defined thresholds for determining heat tolerance based on the HTT. Results Using a TCR value of 0.279 [°C/bpm] or less after completing the 120 min HTT can be used as a significant measure to distinguish between heat tolerance and heat intolerance individuals with sensitivity and specificity of 100% of 89%, respectively. In addition, a TCR value of 0.320 [°C/bpm] or less calculated after 60 min was found as a significant measure to determine heat tolerance with 100% sensitivity and 69% specificity. The latter threshold may assist in significantly shortening the HTT for those individuals whose TCR value matches this criterion. Discussion and conclusion A new index (TCR) that combines the thermal and cardiovascular responses to exercise-heat stress was found to be a valid measure, with high sensitivity and specificity, to support the distinguishing between heat tolerance and heat intolerance individuals following a HTT. Furthermore, the suggested index may enable to shorten the HTT, which will make the test more efficient.

Sleep-deprived individuals appear to have decreased psychological and physical capabilities. Studies have shown how major psychological aspects, such as alertness, complex mental performance, and memory, are strongly affected by sleep deprivation. Military use of psychostimulants dates back many years, especially in units that operate over long hours and deprive soldiers of sleep. During prolonged military operations, pilots are regularly kept awake for hours and days without fulfilling their biological sleep requirements. This consequently affects their natural circadian rhythm. This article deals with both the benefits and the side effects of two kinds of psychostimulants, namely, dextroamphetamine, which is more popular and is most widely used, and modafinil, which is a relatively newer type. There is growing evidence that modafinil has fewer side effects, in comparison with its predecessor dextroamphetamine, while still maintaining all of the latter's beneficial characteristics.