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Multiple factors contribute to variation in disease burden, including the type and quality of data, and inherent properties of the models used. Understanding how these factors affect mortality estimates is crucial, especially in the context of public health decision making. We examine how the quality of the studies selected to provide mortality data, influence estimates of burden and provide recommendations about the inclusion of studies and datasets to calculate mortality estimates. To determine how mortality estimates are affected by the data used to generate model outputs, we compared the studies used by The Institute of Health Metrics and Evaluation (IHME) and Maternal and Child Epidemiology Estimation (MCEE) modelling groups to generate enterotoxigenic Escherichia coli (ETEC) and Shigella-associated mortality estimates for 2016. Guided by an expert WHO Working Group, we applied a modified Newcastle-Ottawa Scale (NOS) to evaluate the quality of studies used by both modelling groups. IHME and MCEE used different sets of ETEC and Shigella studies in their models and the majority of studies were high quality. The distribution of the NOS scores was similar between the two modelling groups. We observed an overrepresentation of studies from some countries in SEAR, AFR and WPR compared to other WHO regions. We identified key differences in study inclusion and exclusion criteria used by IHME and MCEE and discuss their impact on datasets used to generate diarrhoea-associated mortality estimates. Based on these observations, we provide a set of recommendations for future estimates of mortality associated with enteric diseases.

BACKGROUND In the absence of high-quality data, the death tolls of epidemics, conflicts, and disasters are often estimated using ad hoc methods. One understudied class of methods, which we term the growth rate discontinuity method (GRDM), estimates death tolls by projecting pre-crisis and post-crisis total population estimates using crude growth rates and then subtracting the results. Despite, or perhaps due to, its simplicity, this method is the source of prominent death toll estimates for the Black Death, the 1918 influenza pandemic, the Great Chinese Famine, and the Rwandan genocide, among others. OBJECTIVE In this article, we review the influence and validity of GRDM and its applications. METHODS Using statistical simulation and comparison with better-validated demographic methods, we assess the accuracy, precision, and biases of this method for estimating mortality in absolute and relative terms. RESULTSWe find that existing GRDM estimates often misestimate death tolls by large, unpredictable margins. Simulations suggest this is because GRDM requires precision in its inputs to an extent rarely possible in the contexts of interest. CONCLUSIONS If there is sufficient data to specify GRDM well, it is probably possible to also use a more reliable method; if there is not sufficient data, GRDM estimates are so sensitive to their assumptions that they cannot be considered informative. CONTRIBUTION These findings question the empirical suitability of existing demographic and econometric work that has used GRDM to analyse mortality crises. They also underscore the need for improved data collection in crisis settings and the utility of qualitative methods in contexts where quantification using better-validated methods is not possible.

To investigate the age composition, growth pattern, mortality, and exploitation rate of Triplophysa pseudoscleroptera in the upper reaches of the Yellow River, this study conducted four sampling surveys between 2022 and 2023 to measure the total body length ( L ) and body weight ( W ) of a total of 313 individuals. Age determination was performed using otoliths. The collected samples exhibited a total length ranging from 4.50 cm to 13.80 cm, body weight from 0.98 g to 19.38 g, and age range from 1 to 5 years old. The relationship between total length and body weight was described by W = 0.0082 L 2.9392 for all samples, indicating isometric relative growth pattern observed in this study. The von Bertalanffy growth equation revealed that T. pseudoscleroptera had an asymptotic total length L ∞ of approximately 18.10 cm with a growth coefficient k estimated as 0.209 yr −1 . The exploitation rate ( E ) is concurrently estimated to be 0.3873. The growth characteristic index of T. pseudoscleroptera is 1.8355; in comparison with other fish species within the same genus, it demonstrates a relatively sluggish overall growth rate. Compared with other Triplophysa fish, the growth rate of T. pseudoscleroptera in the upper reaches of the Yellow River appears to be relatively slow, and its mortality rate is predominantly influenced by fishing activities. Therefore, given the relatively high fishing mortality rate and the current situation in the Yellow River for T. pseudoscleroptera , it is imperative to implement locally tailored management strategies to safeguard the population of this species. These strategies should include measures to regulate the introduction of nonindigenous fish and establish regulations on the utilization of finer mesh fishing tools owing to their more obvious detrimental impact on small and medium-sized fish.

Background Estimates of completeness of death registration are crucial to produce estimates of life tables and population projections and to estimate the burden of disease. They are an important step in assessing the quality of data. In the case of subnational data analysis in Brazil, it is important to consider spatial and temporal variation in the quality of mortality data. There are two main sources of data quality evaluation in Brazil, but there are few comparative studies and how they evolve over time. The aim of the paper is to compare and discuss alternative estimates of completeness of death registration, adult mortality (45q15) and life expectancy estimates produced by the National Statistics Office (IBGE), Institute for Health Metrics and Evaluation (IHME), and estimates presented in Queiroz et al. (2017) and Schmertmann and Gonzaga (2018), for 1980 and 2010. Methods We provide a descriptive and comparative analysis of aforementioned estimates from four (4) sources of estimates at subnational level (26 states and one Federal District) in Brazil from two different points in time. Results We found significant differences in estimates that affect both levels and trends of completeness of adult mortality in Brazil and states. IHME and Queiroz et al. (2017) estimates converge by 2010, but there are large differences when compared to estimates from the National Statistics Office (IBGE). Larger differences are observed for less developed states. We have showed that the quality of mortality data in Brazil has improved steadily overtime, but with large regional variations. However, we have observed that IBGE estimates show the lowest levels of completeness for the Northern of the country compared to other estimates. Choice of methods and approaches might lead to very unexpected results. Conclusion We produced a detailed comparative analysis of estimates of completeness of death registration from different sources and discuss the main results and possible explanations for these differences. We have also showed that new improved methods are still needed to study adult mortality in less developed countries and at a subnational level. More comparative studies are important in order to improve quality of estimates in Brazil.

To investigate the age structure, growth pattern, mortality and exploitation rates of Leuciscus chuanchicus in the upstream Ningxia section of the Yellow River, four sampling surveys were conducted between 2022 and 2023. A total of 472 individuals were measured for their total length ( TL ) and body weight ( W ). Age determination was performed using otoliths. The collected samples had a range of total lengths from 4.52 to 37.45 cm, body weights ranging from 0.68 to 552.43 g, and ages ranging from 1 to 7 years old. The relationship between total length and body weight was expressed as W = 0.0052 L 3.19 for all samples, which indicates that the growth of L. chuanchicus adheres to allometry. The Von Bertalanffy growth equation revealed that the fish had an asymptotic total length ( L ∞ ) of approximately 37.9 cm with a growth coefficient ( K ) value of approximately 0.461 yr −1 . Using the age-based catch curve method, the calculated total instantaneous mortality rate ( Z ) for all samples was determined as being equal to approximately 1.1302 yr −1 . Additionally, three methods were used to estimate the average instantaneous rate of natural mortality ( M ), resulting in an approximate value of 0.7167 yr −1 for all samples. Furthermore, the instantaneous rate of fishing mortality ( F ) for all samples was calculated as 0.4134 yr −1 , leading us to determine that the exploitation rate ( E ) is 0.3658. It was concluded that the growth rate of L. chuanchicus in the upstream of the Yellow River is relatively fast, and L. chuanchicus has not been subjected to excessive exploitation, yet its relatively high natural mortality rate underscores the need for targeted management measures aimed at preserving its habitat.

High-dimensional parametric models with penalized likelihood functions strike a good balance between bias and variance for estimating continuous age schedules from large samples. The penalized spline (P-spline) approach is particularly useful for these purposes, but it in small samples it can often produce implausible age schedule estimates. I propose and evaluate a new type of P-spline model for estimating demographic rate schedules. These estimators, which I call D-splines, regularize and smooth high-dimensional splines by using demographic patterns rather than generic mathematical rules. I compare P-spline estimates of age-specific mortality rates to three alternative D-spline estimators, over a large number of simulated small populations with known rates. The penalties for the D-spline estimators are derived from patterns in the Human Mortality Database. For mortality estimates in small populations, D-spline estimators generally have lower errors than standard P-splines. Using penalties based on demographic information about patterns and variability in rate schedules improves P-spline estimators for small populations.

Multiple systems estimation (MSE) is becoming an increasingly common approach for exploratory study of underreported events in the field of quantitative human rights. In this context, it is used to estimate the number of people who died as a result of political unrest when it is believed that many of those who died or disappeared were never reported. MSE relies upon several assumptions, each of which may be slightly or significantly violated in particular applications. This article outlines the evolution of the application of MSE to human rights research through the use of three case studies: Guatemala, Peru, and Colombia. Each of these cases presents distinct challenges to the MSE method. Motivated by these applications, we describe new methodology for assessing the impact of violated assumptions in MSE. Our approach uses simulations to explore the cumulative magnitude of errors introduced by violation of the model assumptions at each stage in the analysis.

The 165-km2 Altamont Pass Wind Resource Area (APWRA) in west-central California includes 5,400 wind turbines, each rated to generate between 40 kW and 400 kW of electric power, or 580 MW total. Many birds residing or passing through the area are killed by collisions with these wind turbines. We searched for bird carcasses within 50 m of 4,074 wind turbines for periods ranging from 6 months to 4.5 years. Using mortality estimates adjusted for searcher detection and scavenger removal rates, we estimated the annual wind turbine–caused bird fatalities to number 67 (80% CI = 25–109) golden eagles (Aquila chrysaetos), 188 (80% CI = 116–259) red-tailed hawks (Buteo jamaicensis), 348 (80% CI = −49 to 749) American kestrels (Falco sparverius), 440 (80% CI = −133 to 1,013) burrowing owls (Athene cunicularia hypugaea), 1,127 (80% CI = −23 to 2,277) raptors, and 2,710 (80% CI = −6,100 to 11,520) birds. Adjusted mortality estimates were most sensitive to scavenger removal rate, which relates to the amount of time between fatality searches. New on-site studies of scavenger removal rates might warrant revising mortality estimates for some small-bodied bird species, although we cannot predict how the mortality estimates would change. Given the magnitude of our mortality estimates, regulatory agencies and the public should decide whether to enforce laws intended to protect species killed by APWRA wind turbines, and given the imprecision of our estimates, directed research is needed of sources of error and bias for use in studies of bird collisions wherever wind farms are developed. Precision of mortality estimates could be improved by deploying technology to remotely detect collisions and by making wind turbine power output data available to researchers so that the number of fatalities can be related directly to the actual power output of the wind turbine since the last fatality search.

There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates. The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data. International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output. Modeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R =0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality. Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs.

Despite the highest burden of Typhoid fever in children globally, exact estimates of morbidity and mortality are lacking due to scarcity of published data. Despite a high prevalence and a socioeconomic burden in developing countries, published data with morbidity and mortality figures are limited especially Africa and South American regions. Data from the community is insufficient and most case fatality estimates are extrapolations from hospital based studies that do not cover all geographical regions, and include cases which may or not be culture confirmed, MDR resistant or sensitive cases, or from mixed populations of age (adults and children). Complications of typhoid such as intestinal perforation, bone marrow suppression, and encephalopathy are dependent on MDR/Fluoroquinolone resistant Salmonella infection, comorbidities such as malnutrition, and health-care access. Data is again insufficient to estimate the true burden of Typhoid Fever in different regions and groups of populations. Although there has been a rapid decline in cases in developed countries with the advent of improved sanitization, timely and easy access to health care and laboratories, this is still not the case in the developing countries where Typhoid deaths are still occurring. The way forward is to develop rapid and cost effective point of care diagnostic tests, put in place validated clinical algorithms for suspected clinical cases, and design prospective, and community based studies in different groups, implement maintenance of electronic health records in large public sector hospitals and regions to identify populations that will benefit most from the implementation of vaccine. Policies on public health education and typhoid vaccine may help to reduce morbidity and mortality due to the disease.