Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc.). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

UNASSIGNED: Mortality statistics are fundamental to understand the magnitude of the COVID-19 pandemic. Due to limitation of real-time data availability, researchers had used mathematical models to estimate excess mortality globally during COVID-19 pandemic. As they demonstrated variations in scope, assumptions, estimations, and magnitude of the pandemic, and hence raised a controversy all over the world. This paper aims to review the mathematical models and their estimates of mortality due to COVID-19 in the Indian context.
UNASSIGNED: The PRISMA and SWiM guidelines were followed to the best possible extent. A two-step search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST). We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs.
UNASSIGNED: Significant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million.
UNASSIGNED: The review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates.

UNASSIGNED: COVID-19 has proven to be the worst pandemic in the history of mankind. While the pandemic still continues to perplex scientists globally, attempts are being made to quantify the mortality caused by the pandemic. Official COVID-19 figures in India grossly understate the true scale of the pandemic in the country. Fatality rates help us understand the severity of a disease, identify at risk populations, and evaluate quality of healthcare. Official COVID-19 mortality figures in India grossly understate the true scale of the pandemic in the country. A COVID-19 death is defined for surveillance purposes as a death resulting from a clinically compatible illness in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease (e.g., trauma) and excess mortality is defined as the difference in the total number of deaths in a crisis compared to those expected under normal conditions.
UNASSIGNED: We did a systematic review of multiple papers on PubMed, Medline, Embase, MedRxiV pre print on excess mortality. Differentiation between model based estimated excess mortality and data based excess mortality was studied.
UNASSIGNED: All the studies showed that the excess mortality was to the tune of almost three times the official figures. The model based excess mortality assumptions showed higher deaths as compared to the data based one. However, there were a lot of discrepancies in the data provided by various states along with variations observed between the two waves as well. Health survey data suggested higher mortality rate as compared to data compiled from the civil registration system. Additionally, in the second wave, a small but a significant number of deaths occurred due to non availability of oxygen and beds in the hospitals.
UNASSIGNED: Official COVID-19 deaths have entirely failed to capture the scale of pandemic excess mortality in India. If most excess deaths were, indeed, from COVID-19 then under ascertainment of COVID-19 deaths has been high, with around 8-10 excess deaths for every recorded COVID-19 death.

UNASSIGNED: With the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.
UNASSIGNED: We searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.
UNASSIGNED: Of 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06-0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38-1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07-1.30, p < 0.00001). There was \"very low\" certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.
UNASSIGNED: The COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.
UNASSIGNED: [https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

BACKGROUND: Currently, reported COVID-19 deaths are inadequate to assess the impact of the pandemic on global excess mortality. All-cause excess mortality is a WHO-recommended index for assessing the death burden of COVID-19. However, the global excess mortality assessed by this index remains unclear. We aimed to assess the global excess mortality during the COVID-19 pandemic.
METHODS: We searched PubMed, EMBASE, and Web of Science for studies published in English between 1 January 2020, and 21 May 2022. Cross-sectional and cohort studies that reported data about excess mortality during the pandemic were included. Two researchers independently searched the published studies, extracted data, and assessed quality. The Mantel-Haenszel random-effects method was adopted to estimate pooled risk difference (RD) and their 95% confidence intervals (CIs).
RESULTS: A total of 79 countries from twenty studies were included. During the COVID-19 pandemic, of 2,228,109,318 individuals, 17,974,051 all-cause deaths were reported, and 15,498,145 deaths were expected. The pooled global excess mortality was 104.84 (95% CI 85.56-124.13) per 100,000. South America had the highest pooled excess mortality [134.02 (95% CI: 68.24-199.80) per 100,000], while Oceania had the lowest [-32.15 (95% CI: -60.53--3.77) per 100,000]. Developing countries had higher excess mortality [135.80 (95% CI: 107.83-163.76) per 100,000] than developed countries [68.08 (95% CI: 42.61-93.55) per 100,000]. Lower middle-income countries [133.45 (95% CI: 75.10-191.81) per 100,000] and upper-middle-income countries [149.88 (110.35-189.38) per 100,000] had higher excess mortality than high-income countries [75.54 (95% CI: 53.44-97.64) per 100,000]. Males had higher excess mortality [130.10 (95% CI: 94.15-166.05) per 100,000] than females [102.16 (95% CI: 85.76-118.56) per 100,000]. The population aged ≥ 60 years had the highest excess mortality [781.74 (95% CI: 626.24-937.24) per 100,000].
CONCLUSIONS: The pooled global excess mortality was 104.84 deaths per 100,000, and the number of reported all-cause deaths was higher than expected deaths during the global COVID-19 pandemic. In South America, developing and middle-income countries, male populations, and individuals aged ≥ 60 years had a heavier excess mortality burden.

Ludwig\'s angina is characterized by inflammation of the sublingual and submandibular spaces and is mainly caused by odontogenic infection, which leads to cellulitis of the soft tissues of the floor of the mouth and the neck. This causes asphyxia due to elevation and posterior deviation of the tissues of the floor of the mouth. We report a fatal case of airway obstruction due to Ludwig\'s angina. A woman in her forties who had no physical complications, but had a mental illness, was undergoing outpatient dental treatment for caries in the first premolar of the left mandible. She was admitted to a psychiatric hospital because of insomnia caused by pain, where she developed cardiopulmonary arrest while sleeping and died 14 days after onset of the dental infection. Postmortem computed tomography (PMCT) prior to autopsy showed swelling of the soft tissues-from the floor of the mouth to the oropharyngeal cavity, the supraglottic larynx, and the prevertebral tissue. Autopsy revealed a markedly swollen face and neck, an elevated tongue, and a highly edematous epiglottis and laryngopharyngeal mucosa. There was also cellulitis and abscess of the facial, suprahyoid, and neck musculature, which suggested that the cause of death was asphyxiation due to airway obstruction. This was an alarming case, with mental illness leading to risk of severe odontogenic infection, and in which obesity and use of antipsychotic medication might have acted synergistically leading to airway obstruction. This is also a case of Ludwig\'s angina captured by PMCT, which has rarely been reported.

It is almost ten years since the Banerjee 2009 report established that inappropriate prescribing of antipsychotics in the elderly was occurring in the UK and such patients had an 85% increased risk of adverse events and greater mortality. This report was a critical analysis addressing the outcomes of treatment practices for dementia in UK patients and globally, aimed at reducing prescribing of antipsychotic drugs for dementia. Since 2009, many significant studies worldwide (including several more recent large retrospective studies) provide more extensive longitudinal data for the adverse impacts of antipsychotic drugs in dementia. We have used the data in these studies including from over 380,000 dementia patients, with 85,069 prescribed antipsychotic agents as well as from 359,235 non-dementia antipsychotic drug users to provide an up-dated meta-analysis. This is the first meta-analysis to include evidence from general mental health studies showing that antipsychotic drugs precipitate excessive mortality across the spectrum. Prescribing of antipsychotic drugs for dementia or for other mental health care should be avoided and alternative means sought for handling behavioral disorders of such patients.

Mounting epidemiological evidence supports the occurrence of a mild herald pandemic wave in the spring and summer of 1918 in North America and Europe, several months before the devastating autumn outbreak that killed an estimated 2% of the global population. These epidemiological findings corroborate the anecdotal observations of contemporary clinicians who reported widespread influenza outbreaks in spring and summer 1918, with sporadic occurrence of unusually severe clinical manifestations in young adults. Initially seen as controversial, these findings were eventually confirmed by retrospective identification of influenza specimens collected from U.S. soldiers who died from acute respiratory infections in May-August 1918. Other studies found that having an episode of influenza illness during the spring herald wave was highly protective in the severe autumn wave. Here, we conduct a systematic review of the clinical, epidemiological, and virological evidence supporting the global occurrence of mild herald waves of the 1918 pandemic and place these historic observations in the context of pandemic preparedness. Taken together, historic experience with the 1918 and subsequent pandemics shows that increased severity in second and later pandemic waves may be the rule rather than the exception. Thus, a sustained pandemic response in the first years following a future pandemic is critical; conversely, multiwave pandemic patterns allow for more time to rollout vaccines and antivirals.

In order to determine the excess postperinatal mortality rate (PPMR) in the North East Essex health district, researchers examined 108 infant deaths. The PPMR of 10.2/1000 for army infants was significantly higher than the rate of 5.1/1000 for all the other infants (p=.02). Additionally, 8 of the 46 infants that died due to sudden infant death syndrome (SIDS) were children of army personnel. The SIDS rate of 5.8/1000 for army infants exceeded the SIDS rate of 2.1/1000 for all other infants by almost 3 times. Since the death rate for all other causes was 4.4/1000 for army infants and 3.1/1000 for all other infants, researchers concluded that SIDS was the leading cause of postneonatal mortality for army infants. Differences between Army mothers and all other mothers included that Army mothers tend to be younger (mean age 21 v. 26), more smoke (57.% v. 53.2%), and fewer intended to breast feed their infants (35.7% v. 53.2%). In addition, more army families experienced marital stress or violence, or both (p=.05) than did other families. The researchers speculate that for Army mothers, their young age, their isolation in the garrison, and the lack of family support probably all contributed to the high mortality of these infants. The government is considering studying infant deaths since 1984 to learn why they occurred and how to prevent them.