scholarly journals Mortality and Excess Mortality: Improving FluMOMO

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Luís Portugal
Keyword(s):  
Excess Mortality ◽  
Linear Models ◽  
Negative Binomial ◽  
Heat Waves ◽  
Activity Index ◽  
Autoregressive Process ◽  
Extreme Temperatures ◽  
Linear Predictor ◽  
Risk Of Death ◽  
Influenza Activity

FluMOMO is a universal formula to forecast mortality in 27 European countries and was developed on EuroMOMO context, http://www.euromomo.eu. The model has a trigonometric baseline and considers any upwards deviation from that to come from flu or extreme temperatures. To measure it, the model considers two variables: influenza activity and extreme temperatures. With the former, the model gives the number of deaths because of flu and with the latter the number of deaths because of extreme temperatures. In this article, we show that FluMOMO lacks important variables to be an accurate measure of all-cause mortality and flu mortality. Indeed, we found, as expected, that population ageing and exposure to the risk of death cannot be excluded from the linear predictor. We model weekly deaths as an autoregressive process (lag of one together with a lead of one week). This step allowed us to avoid FluMOMO trigonometric baseline and have a fit to weekly deaths through demographic variables. Our model uses data from Portugal between 2009 and 2020, on ISO-week basis. We use negative binomial-generalized linear models to estimate the weekly number of deaths as an alternative to traditional overdispersion Poisson. As explanatory variables were found to be statistically significant, we registered the number of deaths from the previous week, the influenza activity index, the population average age, the heat waves, the flu season, the number of deaths with COVID-19, and the population exposed to the risk of dying. Considering as excess mortality the number of deaths above the best estimate of deaths from our model, we conclude that excess mortality in 2020 (net of COVID-19 deaths, heat wave of July, and ageing) is low or inexistent. The model also allows us to have the number of deaths arising from flu and we conclude that FluMOMO is overestimating deaths from flu by 78%. Averages from the probability of dying are obtained as well as the probability of dying from flu. The latter is shown to be decreasing over time, probably due to the increase of flu vaccination. Higher mortality detected with the start of COVID-19, in March-April 2020, was probably due to COVID-19 deaths not recognized as COVID-19 deaths.

scholarly journals EXCESS DEATHS FROM ALL CAUSES AND BY COVID-19 IN BRAZIL IN 2020

2021 ◽  
Author(s):  
Alcione Miranda dos Santos ◽  
Bruno Feres de Souza ◽  
Carolina Abreu de Carvalho ◽  
Marcos Adriano Garcia Campos ◽  
Bruno Luciano Carneiro Alves de Oliveira ◽  
...  
Keyword(s):  
Racial Differences ◽  
Respiratory Diseases ◽  
Excess Mortality ◽  
Negative Binomial ◽  
Mortality Rates ◽  
Risk Of Death ◽  
External Causes ◽  
Log Linear ◽  
Restrictive Measures ◽  
Excess Deaths

SUMMARYObjectiveTo estimate the 2020 all-cause and COVID-19 excess mortality according to sex, age, race/color, and state, and to compare mortality rates by selected causes with that of the five previous years in Brazil.MethodsData from the Mortality Information System were used. Expected deaths for 2020 were estimated from 2015 to 2019 data using a negative binomial log-linear model.ResultsExcess deaths in Brazil in 2020 amounted to 13.7%, and the ratio of excess deaths to COVID-19 deaths was 0.90. Reductions in deaths from cardiovascular diseases (CVD), respiratory diseases, and external causes, and an increase in ill-defined causes were all noted. Excess deaths were also found to be heterogeneous, being higher in the Northern, Center-Western, and Northeastern states. In some states, the number of COVID-19 deaths was lower than that of excess deaths, whereas the opposite occurred in others. Moreover, excess deaths were higher in men, in those aged 20 to 59, and in black, yellow, or indigenous individuals. Meanwhile, excess mortality was lower in women, individuals aged 80 years or older, and in whites. Additionally, deaths among those aged 0 to 19 were 7.2% lower than expected, with reduction in mortality from respiratory diseases and external causes. There was also a drop in mortality due to external causes in men and in those aged 20 to 39 years. Furthermore, reductions in deaths from CVD and neoplasms were noted in some states and groups.ConclusionThere is evidence of underreporting of COVID-19 deaths and of the possible impact of restrictive measures in the reduction of deaths from external causes and respiratory diseases. The impacts of COVID-19 on mortality were heterogeneous among the states and groups, revealing that regional, demographic, socioeconomic, and racial differences expose individuals in distinct ways to the risk of death from both COVID-19 and other causes.

scholarly journals Excess deaths from all causes and by COVID-19 in Brazil in 2020

2021 ◽  
Vol 55 ◽  
pp. 71
Author(s):  
Alcione Miranda dos Santos ◽  
Bruno Feres de Souza ◽  
Carolina Abreu de Carvalho ◽  
Marcos Adriano Garcia Campos ◽  
Bruno Luciano Carneiro Alves de Oliveira ◽  
...  
Keyword(s):  
Racial Differences ◽  
Respiratory Diseases ◽  
Excess Mortality ◽  
Negative Binomial ◽  
Mortality Rates ◽  
Risk Of Death ◽  
External Causes ◽  
Log Linear ◽  
Restrictive Measures ◽  
Excess Deaths

OBJECTIVE To estimate the 2020 all-cause and COVID-19 excess mortality according to sex, age, race/color, and state, and to compare mortality rates by selected causes with that of the five previous years in Brazil. METHODS Data from the Mortality Information System were used. Expected deaths for 2020 were estimated from 2015 to 2019 data using a negative binomial log-linear model. RESULTS Excess deaths in Brazil in 2020 amounted to 13.7%, and the ratio of excess deaths to COVID-19 deaths was 0.90. Reductions in deaths from cardiovascular diseases (CVD), respiratory diseases, and external causes, and an increase in ill-defined causes were all noted. Excess deaths were also found to be heterogeneous, being higher in the Northern, Center-Western, and Northeastern states. In some states, the number of COVID-19 deaths was lower than that of excess deaths, whereas the opposite occurred in others. Moreover, excess deaths were higher in men aged 20 to 59, and in black, yellow, or indigenous individuals. Meanwhile, excess mortality was lower in women, in individuals aged 80 years or older, and in whites. Additionally, deaths among those aged 0 to 19 were 7.2% lower than expected, with reduction in mortality from respiratory diseases and external causes. There was also a drop in mortality due to external causes in men and in those aged 20 to 39 years. Moreover, reductions in deaths from CVD and neoplasms were noted in some states and groups. CONCLUSION There is evidence of underreporting of COVID-19 deaths and of the possible impact of restrictive measures in the reduction of deaths from external causes and respiratory diseases. The impacts of COVID-19 on mortality were heterogeneous among the states and groups, revealing that regional, demographic, socioeconomic, and racial differences expose individuals in distinct ways to the risk of death from both COVID-19 and other causes.

scholarly journals A heat-health watch and warning system with extended season and evolving thresholds

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahamat Abdelkerim Issa ◽  
Fateh Chebana ◽  
Pierre Masselot ◽  
Céline Campagna ◽  
Éric Lavigne ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Heat Waves ◽  
Meteorological Data ◽  
Warm Season ◽  
Warning System ◽  
Early Warning Systems ◽  
Climate Projections ◽  
Warning Systems ◽  
Extreme Heat Events ◽  
Watch And Warning System

Abstract Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

scholarly journals Development and validation of the predictive risk of death model for adult patients admitted to intensive care units in Japan: an approach to improve the accuracy of healthcare quality measures

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hideki Endo ◽  
Shigehiko Uchino ◽  
Satoru Hashimoto ◽  
Yoshitaka Aoki ◽  
Eiji Hashiba ◽  
...  
Keyword(s):  
Intensive Care ◽  
Quality Of Care ◽  
Generalized Linear Model ◽  
Healthcare Quality ◽  
Brier Score ◽  
Linear Predictor ◽  
Risk Of Death ◽  
Apache Iii ◽  
Funnel Plots

Abstract Background The Acute Physiology and Chronic Health Evaluation (APACHE) III-j model is widely used to predict mortality in Japanese intensive care units (ICUs). Although the model’s discrimination is excellent, its calibration is poor. APACHE III-j overestimates the risk of death, making its evaluation of healthcare quality inaccurate. This study aimed to improve the calibration of the model and develop a Japan Risk of Death (JROD) model for benchmarking purposes. Methods A retrospective analysis was conducted using a national clinical registry of ICU patients in Japan. Adult patients admitted to an ICU between April 1, 2018, and March 31, 2019, were included. The APACHE III-j model was recalibrated with the following models: Model 1, predicting mortality with an offset variable for the linear predictor of the APACHE III-j model using a generalized linear model; model 2, predicting mortality with the linear predictor of the APACHE III-j model using a generalized linear model; and model 3, predicting mortality with the linear predictor of the APACHE III-j model using a hierarchical generalized additive model. Model performance was assessed with the area under the receiver operating characteristic curve (AUROC), the Brier score, and the modified Hosmer–Lemeshow test. To confirm model applicability to evaluating quality of care, funnel plots of the standardized mortality ratio and exponentially weighted moving average (EWMA) charts for mortality were drawn. Results In total, 33,557 patients from 44 ICUs were included in the study population. ICU mortality was 3.8%, and hospital mortality was 8.1%. The AUROC, Brier score, and modified Hosmer–Lemeshow p value of the original model and models 1, 2, and 3 were 0.915, 0.062, and < .001; 0.915, 0.047, and < .001; 0.915, 0.047, and .002; and 0.917, 0.047, and .84, respectively. Except for model 3, the funnel plots showed overdispersion. The validity of the EWMA charts for the recalibrated models was determined by visual inspection. Conclusions Model 3 showed good performance and can be adopted as the JROD model for monitoring quality of care in an ICU, although further investigation of the clinical validity of outlier detection is required. This update method may also be useful in other settings.

scholarly journals Temporal analysis of the relationship between dengue and meteorological variables in the city of Rio de Janeiro, Brazil, 2001-2009

2012 ◽  
Vol 28 (11) ◽  
pp. 2189-2197 ◽  
Author(s):  
Adriana Fagundes Gomes ◽  
Aline Araújo Nobre ◽  
Oswaldo Gonçalves Cruz
Keyword(s):  
Minimum Temperature ◽  
Rio De Janeiro ◽  
Linear Models ◽  
Negative Binomial ◽  
Critical Factor ◽  
Temporal Analysis ◽  
Temporal And Spatial Distribution ◽  
Temperature And Precipitation ◽  
The Relationship ◽  
The City

Dengue, a reemerging disease, is one of the most important viral diseases transmitted by mosquitoes. Climate is considered an important factor in the temporal and spatial distribution of vector-transmitted diseases. This study examined the effect of seasonal factors and the relationship between climatic variables and dengue risk in the city of Rio de Janeiro, Brazil, from 2001 to 2009. Generalized linear models were used, with Poisson and negative binomial distributions. The best fitted model was the one with "minimum temperature" and "precipitation", both lagged by one month, controlled for "year". In that model, a 1°C increase in a month's minimum temperature led to a 45% increase in dengue cases in the following month, while a 10-millimeter rise in precipitation led to a 6% increase in dengue cases in the following month. Dengue transmission involves many factors: although still not fully understood, climate is a critical factor, since it facilitates analysis of the risk of epidemics.

Mortality following new-onset Rheumatoid Arthritis: has modern Rheumatology had an impact?

2017 ◽  
Vol 77 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Marie Holmqvist ◽  
Lotta Ljung ◽  
Johan Askling
Keyword(s):  
Rheumatoid Arthritis ◽  
General Population ◽  
Excess Mortality ◽  
Disease Onset ◽  
Mortality Rates ◽  
Calendar Time ◽  
Risk Of Death ◽  
Quality Register ◽  
Increased Risk ◽  
New Onset

ObjectiveTo investigate if, and when, patients diagnosed with rheumatoid arthritis (RA) in recent years are at increased risk of death.MethodsUsing an extensive register linkage, we designed a population-based nationwide cohort study in Sweden. Patients with new-onset RA from the Swedish Rheumatology Quality Register, and individually matched comparators from the general population were followed with respect to death, as captured by the total population register.Results17 512 patients with new-onset RA between 1 January 1997 and 31 December 2014, and 78 847 matched general population comparator subjects were followed from RA diagnosis until death, emigration or 31 December 2015. There was a steady decrease in absolute mortality rates over calendar time, both in the RA cohort and in the general population. Although the relative risk of death in the RA cohort was not increased (HR=1.01, 95% CI 0.96 to 1.06), an excess mortality in the RA cohort was present 5 years after RA diagnosis (HR after 10 years since RA diagnosis=1.43 (95% CI 1.28 to 1.59)), across all calendar periods of RA diagnosis. Taking RA disease duration into account, there was no clear trend towards lower excess mortality for patients diagnosed more recently.ConclusionsDespite decreasing mortality rates, RA continues to be linked to an increased risk of death. Thus, despite advancements in RA management during recent years, increased efforts to prevent disease progression and comorbidity, from disease onset, are needed.

scholarly journals An Ethology of Adaptation: Dolphins Stop Feeding but Continue Socializing in Construction-Degraded Habitat

2021 ◽  
Vol 8 ◽  
Author(s):  
Ann Weaver
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Environmental Changes ◽  
Bottlenose Dolphins ◽  
Observation Time ◽  
Mixed States ◽  
The West ◽  
Behavioral Adaptations ◽  
Construction Zone ◽  
Free Ranging

Adaptation is a biological mechanism by which organisms adjust physically or behaviorally to changes in their environment to become more suited to it. This is a report of free-ranging bottlenose dolphins’ behavioral adaptations to environmental changes from coastal construction in prime habitat. Construction was a 5-year bridge removal and replacement project in a tidal inlet along west central Florida’s Gulf of Mexico coastline. It occurred in two consecutive 2.5-year phases to replace the west and east lanes, respectively. Lane phases involved demolition/removal of above-water cement structures, below-water cement structures, and reinstallation of below + above water cement structures (N = 2,098 photos). Data were longitudinal (11 years: 2005–2016, N = 1,219 surveys 2–4 times/week/11 years, N = 4,753 dolphins, 591.95 h of observation in the construction zone, 126 before-construction surveys, 568 during-construction surveys, 525 after-construction surveys). The dependent variable was numbers of dolphins (count) in the immediate construction zone. Three analyses examined presence/absence, total numbers of dolphins, and numbers of dolphins engaged in five behavior states (forage-feeding, socializing, direct travel, meandering travel, and mixed states) across construction. Analyses were GLIMMIX generalized linear models for logistic and negative binomial regressions to account for observation time differences as an exposure (offset) variable. Results showed a higher probability of dolphin presence than absence before construction began, more total dolphins before construction, and significant decreases in the numbers of feeding but not socializing dolphins. Significant changes in temporal rhythms also revealed finer-grained adaptations. Conclusions were that the dolphins adapted to construction in two ways, by establishing feeding locations beyond the disturbed construction zone and shifting temporal rhythms of behaviors that they continued to exhibit in the construction zone to later in the day when construction activities were minimized. This is the first study to suggest that the dolphins learned to cope with coastal construction with variable adjustments.

scholarly journals Generalized linear models with random effects for the description of data with excess zeros

2011 ◽  
Vol 54 (6) ◽  
pp. 661-675
Author(s):  
N. Mielenz ◽  
K. Thamm ◽  
M. Bulang ◽  
J. Spilke
Keyword(s):  
Random Effects ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Linear Models ◽  
Negative Binomial ◽  
Detailed Comparison ◽  
Response Scale ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Explanatory Factors

Abstract. In this paper count data with excess zeros and repeated observations per subject are evaluated. If the number of values observed for the zero event in the trial substantially exceeds the expected number (derived from the Poisson or from the negative binomial distribution), then there is an excess of zeros. Hurdle and zero-inflated models with random effects are available in order to evaluate this type of data. In this paper both model approaches are presented and are used for the evaluation of the number of visits to the feeder per cow per hour. Finally, for the analysis of the target trait a hurdle model with random effects based on a negative binomial distribution was used. This analysis was derived from a detailed comparison of models and was needed because of a simpler computer implementation. For improved interpretation of the results, the levels of the explanatory factors (for example, the classes of lactation) were not averaged in the link scale, but rather in the response scale. The deciding explanatory variables for the pattern of visiting activities in the 24-hour cycle are the milking and cleaning times at hours 4, 7, 12 and 20. The highly significant differences in the visiting frequencies of cows of the first lactation and those of higher lactations were explained by competition for access to the feeder and thus to the feed.

scholarly journals Comparison and evaluation of statistical error models for scRNA-seq

2021 ◽  
Author(s):  
Saket Choudhary ◽  
Rahul Satija
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Statistical Error ◽  
Rna Seq ◽  
Multiple Sources ◽  
Error Models ◽  
Wide Range ◽  
Data Driven Approach ◽  
Downstream Analysis ◽  
Experimental Processing

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate. Here, we analyze 58 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation. Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

scholarly journals THE US MIDLIFE MORTALITY CRISIS CONTINUES: INCREASED DEATH RATES FROM CAUSES OTHER THAN COVID-19 DURING 2020

2021 ◽  
Author(s):  
Dana A Glei
Keyword(s):  
Indirect Effect ◽  
Causes Of Death ◽  
Excess Mortality ◽  
Negative Binomial ◽  
National Level ◽  
Age Groups ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Age Group ◽  
External Causes

COVID-19 has prematurely ended many lives, particularly among the oldest Americans, but the pandemic has also had an indirect effect on health and non-COVID mortality among the working-age population, who have suffered the brunt of the economic consequences. This analysis quantifies the changes in mortality for selected causes of death during the COVID 19 pandemic up to December 31, 2020, and investigates whether the levels of excess mortality varied by age group. The data comprise national-level monthly death counts by age group and selected causes of death from January 1999 to December 2020 combined with annual mid-year population estimates over the same period. A negative binomial regression model was used to estimate monthly cause-specific excess mortality during 2020 controlling for the pre-pandemic mortality patterns by age, calendar year, and season. To determine whether excess mortality varied by age, we tested interactions between broad age groups and dichotomous indicators for the pre-pandemic (January-February) and the pandemic (March-December) portions of 2020. In relative terms, excess all cause mortality (including COVID-19) peaked in December at ages 25-44 (RR=1.58 relative to 2019, 95% CI=1.50-1.68). Excluding COVID-19, all of the excess mortality occurred between ages 15 and 64, peaking in July among those aged 25-44 (RR=1.45, 95% CI 1.37-1.53). We find notable excess mortality during March-December 2020 for many causes (i.e., influenza/pneumonia, other respiratory diseases, diabetes, heart disease, cerebrovascular disease, kidney disease, and external causes), but almost exclusively among young and midlife (aged 25-74) Americans. For those aged 75 and older, there was little excess mortality from causes other than COVID-19 except from Alzheimer's disease. Excess non-COVID mortality may have resulted, at least partly, from incorrectly classified COVID-19 deaths, but neither misclassification nor an atypical flu season that disproportionately affected younger people is likely to explain the increase in mortality from external causes, which was evident even during January-February 2020. Exploratory analyses suggest that drug-related mortality may be driving the early rise in external mortality. The growth in drug overdoses well before there was any hint of a pandemic suggests it is probably not solely an indirect effect of COVID-19, although the pandemic may have exacerbated the problem.

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