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This paper used Our World data for coronavirus disease-2019 (COVID-19) death count, test data, stringency, and transmission count and prepared a path model for COVID-19 deaths. We augmented the model with age structure-related variables and comorbidity via non-communicable diseases for 117 countries of the world for September 23, 2021, on a cross-section basis. A broad-based global quantitative study incorporating these two prominent channels with regional variation was unavailable in the existing literature. Old age and comorbidity were identified as two prime determinants of COVID-19 mortality. The path model showed that after controlling for these factors, one SD increase in the proportion of persons above 65, above 70, or of median age raised COVID-19 mortality by more than 0.12 SDs for 117 countries. The regional intensity of death is alarmingly high in South America, Europe, and North America compared with Oceania. After controlling for regions, the figure was raised to 0.213, which was even higher. For old age, the incremental coefficient was the highest for South America (0.564), and Europe (0.314), which were substantially higher than in Oceania. The comorbidity channel via non-communicable diseases illustrated that one SD increase in non-communicable disease intensity increased COVID-19 mortality by 0.132 for the whole sample. The regional figure for the non-communicable disease was 0.594 for South America and 0.358 for Europe compared with the benchmark region Oceania. The results were statistically significant at a 10% level of significance or above. This suggested that we should prioritize vaccinations for the elderly and people with comorbidity via non-communicable diseases like heart disease, cancer, chronic respiratory disease, and diabetes. Further attention should be given to South America and Europe, which are the worst affected regions of the world.

Jon Pedersen and Jing Liu examine the feasibility and potential advantages of using one-year rather than five-year time periods along with calendar year-based estimation when deriving estimates of child mortality., Background Child mortality estimates from complete birth histories from Demographic and Health Surveys (DHS) surveys and similar surveys are a chief source of data used to track Millennium Development Goal 4, which aims for a reduction of under-five mortality by two-thirds between 1990 and 2015. Based on the expected sample sizes when the DHS program commenced, the estimates are usually based on 5-y time periods. Recent surveys have had larger sample sizes than early surveys, and here we aimed to explore the benefits of using shorter time periods than 5 y for estimation. We also explore the benefit of changing the estimation procedure from being based on years before the survey, i.e., measured with reference to the date of the interview for each woman, to being based on calendar years. Methods and Findings Jackknife variance estimation was used to calculate standard errors for 207 DHS surveys in order to explore to what extent the large samples in recent surveys can be used to produce estimates based on 1-, 2-, 3-, 4-, and 5-y periods. We also recalculated the estimates for the surveys into calendar-year-based estimates. We demonstrate that estimation for 1-y periods is indeed possible for many recent surveys. Conclusions The reduction in bias achieved using 1-y periods and calendar-year-based estimation is worthwhile in some cases. In particular, it allows tracking of the effects of particular events such as droughts, epidemics, or conflict on child mortality in a way not possible with previous estimation procedures. Recommendations to use estimation for short time periods when possible and to use calendar-year-based estimation were adopted in the United Nations 2011 estimates of child mortality., Editors' Summary Background In 2000, world leaders set, as Millennium Development Goal 4 (MDG 4), a target of reducing global under-five mortality (the number of children who die before their fifth birthday to a third of its 1990 level (12 million deaths per year) by 2015. (The MDGs are designed to alleviate extreme poverty by 2015.) To track progress towards MDG 4, the under-five mortality rate (also shown as 5 q 0) needs to be estimated both “precisely” and “accurately.” A “precise” estimate has a small random error (a quality indicated by a statistical measurement called the coefficient of variance), and an “accurate” estimate is one that is close to the true value because it lacks bias (systematic errors). In an ideal world, under-five mortality estimates would be based on official records of births and deaths. However, developing countries, which are where most under-five deaths occur, rarely have such records, and under-five mortality estimation relies on “complete birth histories” provided by women via surveys. These are collected by Demographic and Health Surveys (DHS, a project that helps developing countries collect data on health and population trends) and record all the births that a surveyed woman has had and the age at death of any of her children who have died. Why Was This Study Done? Because the DHS originally surveyed samples of 5,000–6,000 women, estimates of under-five mortality are traditionally calculated using data from five-year time periods. Over shorter periods with this sample size, the random errors in under-five mortality estimates become unacceptably large. Nowadays, the average DHS survey sample size is more than 10,000 women, so it should be possible to estimate under-five mortality over shorter time periods. Such estimates should be able to track the effects on under-five mortality of events such as droughts and conflicts better than estimates made over five years. In this study, the researchers determine appropriate time periods for child mortality estimates based on full birth histories, given different sample sizes. Specifically, they ask whether, with the bigger sample sizes that are now available, details about trends in under-five mortality rates are being missed by using the estimation procedures that were developed for smaller samples. They also ask whether calendar-year-based estimates can be calculated; mortality is usually estimated in “years before the survey,” a process that blurs the reference period for the estimate. What Did the Researchers Do and Find? The researchers used a statistical method called “jackknife variance estimation” to determine coefficients of variance for child mortality estimates calculated over different time periods using complete birth histories from 207 DHS surveys. Regardless of the estimation period, half of the estimates had a coefficient of variance of less than 10%, a level of random variation that is generally considered acceptable. However, within each time period, some estimates had very high coefficients of variance. These estimates were derived from surveys where there was a small sample size, low fertility (the women surveyed had relatively few babies), or low child mortality. Other analyses show that although the five-year period estimates had lower standard errors than the one-year period estimates, the latter were affected less by bias than the five-year period estimates. Finally, estimates fixed to calendar years rather than to years before the survey were more directly comparable across surveys and brought out variations in child mortality caused by specific events such as conflicts more clearly. What Do These Findings Mean? These findings show that although under-five mortality rate estimates based on five-year periods of data have been the norm, the sample sizes currently employed in DHS surveys make it feasible to estimate mortality for shorter periods. The findings also show that using shorter periods of data in estimations of the under-five mortality rate, and using calendar-year-based estimation, reduces bias (makes the estimations more accurate) and allows the effects of events such as droughts, epidemics, or conflict on under-five mortality rates to be tracked in a way that is impossible when using five-year periods of data. Given these findings, the researchers recommend that time periods shorter than five years should be adopted for the estimation of under-five mortality and that estimations should be pegged to calendar years rather than to years before the survey. Both recommendations have already been adopted by the United Nations Inter-agency Group for Child Mortality Estimation (IGME) and were used in their 2011 analysis of under-five mortality. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001289. This paper is part of a collection of papers on Child Mortality Estimation Methods published in PLOS Medicine The United Nations Childrens Fund (UNICEF) works for children's rights, survival, development, and protection around the world; it provides information on Millennium Development Goal 4, and its Childinfo website provides detailed statistics about child survival and health, including a description of the United Nations Inter-agency Group for Child Mortality Estimation; the 2011 IGME report on Levels and Trends in Child Mortality is available The World Health Organization also has information about Millennium Development Goal 4 and provides estimates of child mortality rates (some information in several languages) Further information about the Millennium Development Goals is available Information is also available about Demographic and Health Surveys of infant and child mortality

A widely-used method for the estimation of numbers of deaths of wildfowl caused by the ingestion of spent lead gunshot pellets depends entirely upon field experiments on wild Mallards conducted 70 years ago in the USA by Frank C. Bellrose. The validity of this method has recently been questioned because of the simple method used to estimate the annual mortality caused by the experimental administration of lead gunshot and because the administration of shot appeared to increase the risk that treated birds would be shot by hunters, which might bias the mortality estimates. In this paper, we report a re-analysis of Bellrose’s data using a different method which removes the possible effect on mortality estimates of differences in the proportion of birds killed by hunters in the first year after release. Our results indicate that the additional mortality caused by the administration of lead gunshot in the first year after treatment was substantial and dependent upon the dose of gunshot administered, to a similar extent as was found by Bellrose.

Objective: Coronavirus disease 2019 (COVID-19) has emerged in China at the end of the year 2019 and a few weeks later it has out broken globally. There are obvious geographical variations in the mortality rates of COVID-19 according to the data published by the World Health Organization and Centers for Disease Control and Prevention. This paper aims to estimate where Saudi Arabia stands in comparison to other countries in regard to aspects such as recovery and death percentage among the top 15 countries which have reported the highest number of cases globally. Methods: The percentage of death and recovery from COVID-19 in 15 countries until May 24, 2020, was calculated using data released from the world meter. Results: The highest number of cases was reported in the United States and the lowest cases were in Saudi Arabia. Saudi Arabia demonstrated the lowest number of the death rate which was 0.54%, on the other hand, France ranked first in the mortality rate which was 15.54%. Conclusion: All of the guidelines, precautionary approaches, and instructions included in the orders in addition to the application of the restrictive measures, as well as and high capacity of Saudi hospitals, collectively helped Saudi Arabia to be the leader in achieving the lowest death rate.

A mathematical model of the death rate of malaria parasite due to the antimalarial drugs is established in this paper to predict the treatment duration and the drug dosage. This model is based on probabilities, pharmacokinetics and pharmacodynamics (PK/PD), biology, medical sciences, theoretical physics and chemistry. The death rate of malaria parasite is derived from the flow rates of drug molecules to the malaria parasite by using the theory of blood convection and probabilities. Using the malaria model, numerical results are generated. In conclusion, the numerical estimate of treatment duration from our model ranges from 1 to 10 days, conforming to actual clinical data.

Aim of this paper is to describe the protocol of the study “Impact of a Community-based Program on Prevention and Mitigation of Frailty in communitydwelling older adults” developed in the framework of the European Innovation Partnership on Active and Healthy Ageing. This proposal has been developed by the Partnership Action groups on frailty, fall prevention and polypharmacy in older. The proposal wants to assess the impact of community-based programs aimed to counteract three main outcomes related to frailty: hospitalization, institutionalization and death. Bringing together researchers from seven European countries, the proposal aims to achieve the critical mass and the geographical extension enough to provide information useful to all older European citizens. An observational study will be carried out to calculate the incidence of the different outcomes in relation to the various interventions that will be assessed; results will be compared with data coming from already established national, regional and local dataset using the observed/expected approach. The sample will be made up by at least 2000 citizens for each outcome. All the citizens will be assessed at the baseline with two multidimensional questionnaires: the RISC questionnaire and the Short Functional Geriatric Evaluation questionnaire. The outcomes will be assessed every six-twelve months.

BACKGROUND: Since launching of antiretroviral (ART) treatment, the numbers of patients enrolled in to ART are increasing in many developing countries. But many studies done across Africa including Ethiopia on antiretroviral therapy programs have shown higher mortality at the first six months of treatment initiation. But the factors associated with this high mortality are poorly characterized. So this study aims to determine mortality and identify predictors of it among patients on ART. METHODS: Retrospective cohort study was employed among a total of 520 records of patients who were enrolled on antiretroviral therapy in Aksum hospital from September 2006 to August 2011. Baseline patient records were extracted from electronic and paper based medical records database and analysed using Kaplan Meier survival and Cox proportional hazard model to identify the independent predictors of mortality of patients on ART. RESULTS: A total of 46 (8.85%) deaths was observed giving an overall mortality rate of 3.2 per 100 person-years. The independent predictor of mortality identified for this cohort were haemoglobin level

The worldwide spread of a novel influenza A (H1N1) virus in 2009 showed that influenza remains a significant health threat, even for individuals in the prime of life. This paper focuses on the unusually high young adult mortality observed during the Spanish flu pandemic of 1918. Using historical records from Canada and the U.S., we report a peak of mortality at the exact age of 28 during the pandemic and argue that this increased mortality resulted from an early life exposure to influenza during the previous Russian flu pandemic of 1889-90. We posit that in specific instances, development of immunological memory to an influenza virus strain in early life may lead to a dysregulated immune response to antigenically novel strains encountered in later life, thereby increasing the risk of death. Exposure during critical periods of development could also create holes in the T cell repertoire and impair fetal maturation in general, thereby increasing mortality from infectious diseases later in life. Knowledge of the age-pattern of susceptibility to mortality from influenza could improve crisis management during future influenza pandemics.

The objectives of this paper are to predict life years lost associated with obesity-related diseases (ORDs) for U.S. non-smoking adults, and to examine the relationship between those ORDs and mortality. Data from the National Health Interview Survey, 1997-2000, were used. We employed mixed proportional hazard models to estimate the association between those ORDs and mortality and used simulations to project life years lost associated with the ORDs. We found that obesity-attributable comorbidities are associated with large decreases in life years and increases in mortality rates. The life years lost associated with ORDs is more marked for younger adults than older adults, for blacks than whites, for males than females, and for the more obese than the less obese. Using U.S. non-smoking adults aged 40 to 49 years as an example to illustrate percentage of the life years lost associated with ORDs, we found that the mean life years lost associated with ORDs for U.S. non-smoking black males aged 40 to 49 years with a body mass index above 40 kg/m(2) was 5.43 years, which translates to a 7.5% reduction in total life years. White males of the same age range and same degree of obesity lost 5.23 life years on average - a 6.8% reduction in total life years, followed by black females (5.04 years, a 6.5% reduction in life years), and white females (4.7 years, a 5.8% reduction in life years). Overall, ORDs increased chances of dying and lessened life years by 0.2 to 11.7 years depending on gender, race, BMI classification, and age.

The neutral assumption that individuals of either the same or different species share exactly the same birth, death, migration, and speciation probabilities is fundamental yet controversial to the neutral theory. Several theoretical studies have demonstrated that a slight difference in species per capita birth or death rates can have a profound consequence on species coexistence and community structure. Whether asymmetry in migration, a vital demographic parameter in the neutral model, plays an important role in community assembly still remains unknown. In this paper, we relaxed the ecological equivalence assumption of the neutral model by introducing differences into species regional dispersal ability. We investigated the effect of asymmetric dispersal on the neutral local community structure. We found that per capita asymmetric dispersal among species could reduce species richness of the local community and result in deviations of species abundance distributions from those predicted by the neutral model. But the effect was moderate compared with that of asymmetries in birth or death rates, unless very large asymmetries in dispersal were assumed. A large difference in species dispersal ability, if there is, can overwhelm the role of random drift and make local community dynamics deterministic. In this case, species with higher regional dispersal abilities tended to dominate in the local community. However, the species abundance distribution of the local community under asymmetric dispersal could be well fitted by the neutral model, but the neutral model generally underestimated the fundamental biodiversity number but overestimated the migration rate in such communities.

BACKGROUND: Influenza epidemics have a substantial impact on human health, by increasing the mortality from pneumonia and influenza, respiratory and circulatory diseases, and all causes. This paper provides estimates of excess mortality rates associated with influenza virus circulation for 7 causes of death and 8 age groups in Portugal during the period of 1980-2004. METHODOLOGY/PRINCIPAL FINDINGS: We compiled monthly mortality time series data by age for all-cause mortality, cerebrovascular diseases, ischemic heart diseases, diseases of the respiratory system, chronic respiratory diseases, pneumonia and influenza. We also used a control outcome, deaths from injuries. Age- and cause-specific baseline mortality was modelled by the ARIMA approach; excess deaths attributable to influenza were calculated by subtracting expected deaths from observed deaths during influenza epidemic periods. Influenza was associated with a seasonal average of 24.7 all-cause excess deaths per 100,000 inhabitants, approximately 90% of which were among seniors over 65 yrs. Excess mortality was 3-6 fold higher during seasons dominated by the A(H3N2) subtype than seasons dominated by A(H1N1)/B. High excess mortality impact was also seen in children under the age of four years. Seasonal excess mortality rates from all the studied causes of death were highly correlated with each other (Pearson correlation range, 0.65 to 0.95, P0.64, P