All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021)

Official COVID-19 mortality statistics are strongly influenced by the local diagnostic capacity, strength of the healthcare system, and the recording and reporting capacities on causes of death. This can result in significant undercounting of COVID-19 attributable deaths, making it challenging to understand the total mortality burden of the pandemic. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock of the COVID-19 pandemic. Here, we use data from civil death registers for 54 municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to monthly data from January 2019 to February 2020, we estimate excess mortality over the course of the pandemic from March 2020 to April 2021. We estimated 16,000 [95% CI: 14,000, 18,000] excess deaths across these municipalities since March 2020. The sharpest increase in deaths was observed in April 2021, with an estimated 480% [95% CI: 390%, 580%] increase in mortality from expected counts for the same period. Females and the 40 to 60 age groups experienced a greater increase from baseline mortality compared to other demographic groups. Our excess mortality estimate for these 54 municipalities, representing approximately 5% of the state population, exceeds the official COVID-19 death count for the entire state of Gujarat.

Modeling and Forecasting Under-Five Mortality Rate in Nigeria using Auto-Regressive Integrated Moving Average Approach

Nigeria’s efforts aimed at reducing avoidable child deaths have been met with gradual and sustained progress. Despite the decline in childhood mortality in Nigeria in the last two decades, its prevalence still remain high in comparison to the global standard of mortality for children under the age of five which stands at 25 deaths per 1000 live births. Knowledge of the chances of Nigeria achieving this goal for childhood mortality will aid proper interventions needed to reduce the occurrence. Therefore, this paper employed the Auto-Regressive Integrated Moving Average (ARIMA) model for time series analysis to make forecast of under-five mortality in Nigeria up to 2030 using data obtained from the United Nation’s Inter Agency Group for Childhood Mortality Estimate (UN-IGME). The ARIMA (2, 1, 1) model predicted a reduction of up to 37.3% by 2030 at 95% confidence interval. Results from the study also showed that a reduction of over 300% in under-five mortality is required for Nigeria to be able to achieve the SDG goal for under-five mortality.

Will 5.6 million current American youth eventually die from smoking? The anatomy of a commonly accepted tobacco control measure

BackgroundAccording to the 2014 Surgeon General’s Report (SGR), ‘5.6 million (American) youth currently aged 0–17 years of age will die prematurely of a smoking-related illness.’ Advocates cite this number as evidence that smoking will exact an enormous toll for decades to come. This paper examines whether the projected toll accurately portrays smoking’s likely future burden.MethodsThe SGR estimate, using 2012 state-specific data, can be closely approximated using national data by multiplying the population ages 0–17 by 2012 smoking prevalence among adults ages 18–30, and multiplying that by 32%, the 1996 estimate by the Centers for Disease Control and Prevention of the probability of future smoking-attributable mortality among young adult smokers. Repeating this process using 2018 data estimates the number of future deaths of youth ages 0–17 in 2018. A hypothetical estimate for 2024 assumes continuation of the 2012–2018 smoking prevalence decrease.FindingsBased on 2012 data, the estimated number of youth alive in 2012 who will die prematurely from smoking is 5.31 million. With lower young adult smoking prevalence in 2018, the future smoking-related mortality estimate is 3.66 million. For 2024, the estimate is 2.54 million.ConclusionsThe SGR estimate depended on assumptions that no longer held a few years later. Yet advocates for youth smoking prevention cite it frequently today. Considerations such as this paper’s calculations, decreasing smoking initiation rates, increasing cessation, better screening for and treatment of smoking-related diseases, and smoking’s increasing social unacceptability suggest that smoking’s death toll for today’s youth will be far lower than contemporary estimates. How much lower is virtually impossible to estimate.

Carotid Stenting Versus Carotid Endarterectomy: What Did the Carotid Revascularization Endarterectomy Versus Stenting Trial Show and Where Do We Go From Here?

Although rapidly expanding in its use, carotid artery stenting remains a relatively new procedure. Its growth is due, at least in part, to the perceived advantages of a less invasive technique. However, the clinical effectiveness and specific role for stenting in the treatment of carotid occlusive disease are still under evaluation. The primary aim of the randomized clinical trial, Carotid Revascularization Endarterectomy versus Stenting Trial (CREST), was to contrast the relative efficacy of carotid stenting versus carotid endarterectomy in preventing stroke, myocardial infarction, or death during a 30-day periprocedural period or ipsilateral stroke over the follow-up period in patients with symptomatic and asymptomatic extracranial carotid stenosis. The secondary goals were to describe the differential efficacy of the 2 procedures in men and women, contrast periprocedural (30-day) morbidity and postprocedural morbidity and mortality, estimate and contrast the restenosis rates of the 2 procedures, evaluate differences in measures of health-related quality of life and cost-effectiveness, and identify subgroups of participants at differential risk of stenting or surgery. This report summarizes the results obtained from CREST with respect to its primary and secondary aims.

Mortality Decline in Kenya: A Reexamination of Recent Under-Five Mortality Estimate

This study examined the extent of birth displacement and its effect on the under-five mortality estimates in Kenya. Using data from 2003 and 2008/09 Kenya Demographic and Health Surveys, we evaluate the variability of birth displacement by region and place of residence based on the survival status of the child. We compute birth ratios for children born in the 5th calendar year preceding each survey and note the possible effect on under-five mortality estimates. Results show that under-five mortality estimates in 2008/09 survey are smaller than that of a similar period in 2003 survey by 17 percent. Overall, birth ratios for the 5th calendar year were below 100 percent suggesting presence of birth displacement. However, there was no variance in the displacement between surviving and dead children, hence modest impact on the under-five mortality rate. Evidence suggests that the remarkable decline in the under-five mortality rate recorded in 2008/09 is a function of both overestimation of mortality rate in 2003 survey and underestimation in 2008/09 survey. We recommend that data from more than one source be used to interpret under-five mortality decline and further research should be conducted linking the observed mortality decline to the delivery of known effective interventions.

How does seasonal variability in growth, recruitment, and mortality affect the performance of length-based mortality and asymptotic length estimates in aquatic resources?

Hufnagl, M., Huebert, K. B., and Temming, A. 2013. How does seasonal variability in growth, recruitment, and mortality affect the performance of length-based mortality and asymptotic length estimates in aquatic resources? – ICES Journal of Marine Science, 70: 329–341. We tested the sensitivity of eight methods for estimating total mortality from size frequencies (modified Wetherall; Powell; Beverton and Holt; Jones and van Zalinge; Hoenig; Ssentongo and Larkin; seasonal and non-seasonal Length Converted Catch Curve) to violations of basic assumptions, such as seasonal growth, mortality, recruitment and variable asymptotic length L∞ or growth parameter K. For each method, bias was estimated by simulating length frequency distributions with different combinations of known L∞, Z and K values, calculating θ (Z/K) and L∞ estimates, and comparing the true input with the estimated output values. Input mortality was generally underestimated by all methods and in 27% of all simulations no method provided estimates within θ ± 1. Spring recruitment especially negatively influenced the mortality estimate. A decision tree was developed that provides general guidance in selecting appropriate methods despite violated assumptions, but species-specific case studies are recommended. An example of a species-specific study is provided for the brown shrimp, Crangon crangon. Despite inherent limitations for all methods, the results illustrate that estimates of θ and Z for brown shrimp can be improved substantially by selecting suitable methods and correcting for observed bias.

Low-cost estimates of mortality rate from single tag recoveries: addressing short-term trap-happy and trap-shy bias

Conventional single tag-recovery data are widely available for stock assessments, notably of invertebrate fisheries, worldwide. Though not commonly used for this purpose, the times-at-large in single tag-recovery data provide (relatively) direct information about average mortality rate as a sample of survival times. Mortality rate is estimated using simple formulas given as functions of the mean time-at-large of tagged and recaptured animals. Here we extend an earlier time-at-large mortality estimator to address a potentially common source of bias: trap-happy or trap-shy behavior shortly following tag release. A maximum likelihood solution is derived, yielding an unbiased estimate of instantaneous mortality rate where the interval of usable times-at-large for observed recaptures may be truncated on both sides to any biologist-chosen experimental (recapture) time frame. In tests of the new doubly-truncated mortality estimator using simulated tag-recovery times-at-large, omitting the first 8 weeks of recaptures from the mortality estimate largely eliminated the bias introduced by simulated short-term trap-happy and trap-shy behavior. Bias in the mortality estimate declined by an order of magnitude more than the observed increase in standard error.