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Background: Mortality statistics are essential for public health planning, policy and decision-making. However, underreporting of mortality has been a significant concern in Islamic Republic of Iran. Aim: To analyse trends in mortality and crude death rates, and the completeness of death registration in Islamic Republic of Iran. Methods: We obtained and analysed mortality indicators for Islamic Republic of Iran from 2006 to 2021. Results: Completeness of death registration increased consistently from approximately 80% in 2006 to 94% in 2021 for both sexes. However, there were variations in completeness from province to province. Mortality rates were higher for males than for females. Conclusion: Consistency in the completeness of death registration varied significantly among the provinces, therefore, policies and interventions are needed to address these disparities and improve the overall quality and completeness of mortality data in Islamic Republic of Iran.

Mortality patterns experienced in closely related populations show similarities in some aspects and differences in others. Indeed, if a decline in mortality rates among low-mortality countries is observed, it is possible that populations experience different trends through which this decline occurs. Observing mortality rates for ages and over specific time windows, it is evident that the different interactions between the variables age and time influence longevity trends. Therefore, to grasp the complexity of the phenomenon, the similarities or differences in mortality need to be analyzed by considering three dimensions: age, year, and country, simultaneously. With this aim in mind, we propose applying a multidimensional latent clustering approach to multipopulation mortality data in this paper. We investigate some similarities between the mortality experience of different countries, searching for latent structure across these groups. Starting from the observation units represented by single countries, we nest them in higher-level units of clusters. We apply the proposed model to the mortality rates of 20 developed countries using data from 1965 to 2019 from the Human Mortality Database. We present detailed results for the lower mortality cluster, which collects ages from 50 to 60 among all countries of the selected dataset and highlights different mortality trends between the countries.

In various situations, a researcher analyses data stored in a matrix. Often, the information is replicated on different occasions that can be time-varying or refer to different conditions. In these situations, data can be stored in a multi-way array or tensor. In this work, using the Tucker4 model, we apply a tensor-based approach to the mortality by cause of death, hence considering data stored in a four-dimensional array. The dataset here considered is provided by the World Health Organization and refers to causes of death, ages, years, and countries. A deep understanding of changing mortality patterns is fundamental for planning public policies. Knowledge about mortality trends by causes of death and countries can help Governments manage their health care costs and financial planning, including public pensions, and social security schemes. Our analysis reveals that the Tucker4 model allows for extracting meaningful demographic insights, which are useful to understand that the rise in survival during the twentieth century was mostly determined by a reduction of the main causes of death.

(1) Background: Several smaller studies have shown that COVID-19 patients with cancer are at a significantly higher risk of death. Our objective was to compare patients hospitalized for COVID-19 with cancer to those without cancer using national data and to study the effect of cancer on the risk of hospital death and intensive care unit (ICU) admission. (2) Methods: All patients hospitalized in France for COVID-19 in March–April 2020 were included from the French national administrative database, which contains discharge summaries for all hospital admissions in France. Cancer patients were identified within this population. The effect of cancer was estimated with logistic regression, adjusting for age, sex and comorbidities. (3) Results: Among the 89,530 COVID-19 patients, we identified 6201 cancer patients (6.9%). These patients were older and were more likely to be men and to have complications (acute respiratory and kidney failure, venous thrombosis, atrial fibrillation) than those without cancer. In patients with hematological cancer, admission to ICU was significantly more frequent (24.8%) than patients without cancer (16.4%) (p < 0.01). Solid cancer patients without metastasis had a significantly higher mortality risk than patients without cancer (aOR = 1.4[1.3–1.5]), and the difference was even more marked for metastatic solid cancer patients (aOR = 3.6[3.2–4.0]). Compared to patients with colorectal cancer, patients with lung cancer, digestive cancer (excluding colorectal cancer) and hematological cancer had a higher mortality risk (aOR = 2.0[1.6–2.6], 1.6[1.3–2.1] and 1.4[1.1–1.8], respectively). (4) Conclusions: This study shows that, in France, patients with COVID-19 and cancer have a two-fold risk of death when compared to COVID-19 patients without cancer. We suggest the need to reorganize facilities to prevent the contamination of patients being treated for cancer, similar to what is already being done in some countries.

IntroductionNational Health Service hospitals and government agencies are increasingly using mortality rates to monitor the quality of inpatient care. Mortality and Morbidity (M&M) meetings, established to review deaths as part of professional learning, have the potential to provide hospital boards with the assurance that patients are not dying as a consequence of unsafe clinical practices. This paper examines whether and how these meetings can contribute to the governance of patient safety.MethodsTo understand the arrangement and role of M&M meetings in an English hospital, non-participant observations of meetings (n=9) and semistructured interviews with meeting chairs (n=19) were carried out. Following this, a structured mortality review process was codesigned and introduced into three clinical specialties over 12 months. A qualitative approach of observations (n=30) and interviews (n=40) was used to examine the impact on meetings and on frontline clinicians, managers and board members.FindingsThe initial study of M&M meetings showed a considerable variation in the way deaths were reviewed and a lack of integration of these meetings into the hospital's governance framework. The introduction of the standardised mortality review process strengthened these processes. Clinicians supported its inclusion into M&M meetings and managers and board members saw that a standardised trust-wide process offered greater levels of assurance.ConclusionM&M meetings already exist in many healthcare organisations and provide a governance resource that is underutilised. They can improve accountability of mortality data and support quality improvement without compromising professional learning, especially when facilitated by a standardised mortality review process.

Emerging studies highlight the potential influence of different political regimes on COVID-19 mortality statistics reliability. This study has two objectives: first, to analyze COVID-19 mortality datasets and identify accurate sources for post-communist countries of the European Union and former Soviet Union; second, to examine the relations between COVID-19 mortality data quality and democracy levels in these countries. Given limited open access or transparent national data sources in some countries, this analysis seeks to help researchers identify optimal existing sources for these regions. Observed mortality levels during the pandemic were evaluated in relation to democracy levels to explore associations between governance and data reporting practices. Two mortality indicators (excess mortality and undercount ratio of deaths) were analyzed over 2020–2021 based on three international databases: World Health Organization (WHO), United Nations World Population Prospects (UN WPP), and World Mortality Dataset (WMD). These sources were crucial since some post-communist countries’ national statistical offices do not publish mortality data publicly. The Democracy Index from The Economist Intelligence Unit (2019–2021) was used for democracy classification. Countries were grouped based on mortality characteristics, using cluster analysis. Results suggest that lower democracy levels may be a risk factor for transparency in health data reporting.

Mortality is a critical variable in health care research, especially for evaluating medical product safety and effectiveness. However, inconsistencies in the availability and timeliness of death date and cause of death (CoD) information present significant challenges. Conventional sources such as the National Death Index and electronic health records often experience data lags, missing fields, or incomplete coverage, limiting their utility in time-sensitive or large-scale studies. With the growing use of social media, crowdfunding platforms, and web-based memorials, publicly available digital content has emerged as a potential supplementary source for mortality surveillance. Despite this potential, accurate tools for extracting mortality information from such unstructured data sources remain underdeveloped. The aim of the study is to develop scalable approaches using natural language processing (NLP) and large language models (LLMs) for the extraction of mortality information from publicly available web-based data sources, including social media platforms, crowdfunding websites, and web-based obituaries, and to evaluate their performance across various sources. Data were collected from public posts on X (formerly known as Twitter), GoFundMe campaigns, memorial websites (EverLoved and TributeArchive), and web-based obituaries from 2015 to 2022, focusing on US-based content relevant to mortality. We developed an NLP pipeline using transformer-based models to extract key mortality information such as decedent names, dates of birth, and dates of death. We then used a few-shot learning (FSL) approach with LLMs to identify primary and secondary CoDs. Model performance was assessed using precision, recall, F1-score, and accuracy metrics, with human-annotated labels serving as the reference standard for the transformer-based model and a human adjudicator blinded to the labeling source for the FSL model reference standard. The best-performing model obtained a microaveraged F1-score of 0.88 (95% CI 0.86-0.90) in extracting mortality information. The FSL-LLM approach demonstrated high accuracy in identifying primary CoD across various web-based sources. For GoFundMe, the FSL-LLM achieved 95.9% accuracy for primary cause identification compared to 97.9% for human annotators. In obituaries, FSL-LLM accuracy was 96.5% for primary causes, while human accuracy was 99%. For memorial websites, FSL-LLM achieved 98% accuracy for primary causes, with human accuracy at 99.5%. This study demonstrates the feasibility of using advanced NLP and LLM techniques to extract mortality data from publicly available web-based sources. These methods can significantly enhance the timeliness, completeness, and granularity of mortality surveillance, offering a valuable complement to traditional data systems. By enabling earlier detection of mortality signals and improving CoD classification across large populations, this approach may support more responsive public health monitoring and medical product safety assessments. Further work is needed to validate these findings in real-world health care settings and facilitate the integration of digital data sources into national public health surveillance systems.

The impact of natural hazards on nations and societies is a global challenge and concern. Worldwide, studies have been conducted within and between countries, to examine the spatial distribution and temporal evolution of fatalities and their impact on societies. In Brazil, no studies have comprehensively identified the fatalities associated with all natural hazards and their specificities by decade, region, sex, age, and other victim characteristics. This study carries out an in-depth analysis of the Brazilian Mortality Data of the Brazilian Ministry of Health, from 1979 to 2019, identifying the natural hazards that kill the most people in Brazil and their particularities. Lightning is the deadliest natural hazard in Brazil during this period, with a gradual decrease in the number of fatalities. The number of hydro-meteorological fatalities increases from 2000 onwards, with the highest number of fatalities occurring between 2010 and 2019. Although Brazil is a tropical country affected by severe droughts, extreme heat has the lowest number of fatalities compared to other natural hazards. The period from December to March has a higher number of fatalities, and the southeast is the most populous region where most people die. The number of male victims is twice as high as the number of female victims, across all ages groups, and unmarried victims are the most likely to die. It is therefore essential to recognize and disseminate the knowledge about the impact of different natural hazards on communities and societies, namely on people and their livelihoods, in order to assess the challenges and identify opportunities for reducing the effects of natural hazards in Brazil.

Age-specific mortality rates are often disaggregated by different attributes, such as sex, state, and ethnicity. Forecasting age-specific mortality rates at the national and sub-national levels plays an important role in developing social policy. However, independent forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider reconciling forecasts of age-specific mortality rates, extending the methods of Hyndman et al. in 2011 to functional time series, where age is considered as a continuum. The grouped functional time series methods are used to produce point forecasts of mortality rates that are aggregated appropriately across different disaggregation factors. For evaluating forecast uncertainty, we propose a bootstrap method for reconciling interval forecasts. Using the regional age-specific mortality rates in Japan, obtained from the Japanese Mortality Database, we investigate the one- to ten-step-ahead point and interval forecast accuracies between the independent and grouped functional time series forecasting methods. The proposed methods are shown to be useful for reconciling forecasts of age-specific mortality rates at the national and sub-national levels. They also enjoy improved forecast accuracy averaged over different disaggregation factors. Supplementary materials for the article are available online.