CAUSES OF PIGLET DEATH FROM BIRTH TO WEANING

1987 ◽  
Vol 67 (2) ◽  
pp. 543-547 ◽  
Author(s):  
G. W. DYCK ◽  
E. E. SWIERSTRA
Keyword(s):  
Body Weight ◽  
Key Words ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Congenital Abnormalities ◽  
Minor Importance ◽  
Secondary Importance ◽  
Adequate Nutrition ◽  
Underlying Cause Of Death

Causes of piglet death were determined for 569 piglets that died between birth and weaning out of a total of 2388 born over the second to fourth parity in 124 Yorkshire and 109 Yorkshire × Lacombe sow litters. Eight specific causes of death were identified. Starvation, crushing by the sow and stillbirths were the three main causes. Unidentified causes and piglets euthanized largely because of sow death or injury were of secondary importance. Exposure, congenital abnormalities and disease were of minor importance. In addition, the primary underlying cause of death appears to be a lack of adequate nutrition for the piglets as only 6.3% of the piglets dying during the first 3 d had an increase in body weight and only 15.4% of the piglets dying after day 3 had body weight increases that could be considered as adequate for their age. Key words: Piglets, death, birth, lactation

scholarly journals 1000Quantifying multiple causes of death: A systematic review and audit of methods and practice

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Saliu Balogun ◽  
Karen Bishop ◽  
James Eynstone-Hinkins ◽  
Melonie Martin ◽  
Margarita Moreno-Betancur ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Analytical Framework ◽  
Multiple Causes Of Death ◽  
Underlying Cause Of Death ◽  
N 93 ◽  
International Experts ◽  
Analytical Approaches ◽  
Multiple Causes

Abstract Background Mortality reporting and research are typically focused on a single underlying cause of death (UCoD) selected from multiple reported causes. The need to incorporate multiple causes of death (MCoD) in mortality statistics is now recognised internationally, but there is scant methodological work to guide analytical approaches. This review aims to identify and appraise current methods and practices used to analyse MCoD data. Methods The Web of Science, Medline, Pubmed and Scopus (from inception to December 2019) were queried. Studies reporting MCoD alone or in comparison with single UCoD were included. The review is supplemented by qualitative interview with international experts. Results 3491 studies were identified; 141 full texts were included in the review. The measures usually estimated when analysing MCoD can be broadly categorised into descriptive measures (n = 93 studies), measures of associations between diseases (n = 46 studies) and advanced statistical methods (n = 11 studies). Descriptive statistics commonly used include standardized ratio of multiple to underlying cause (SRMU) and mortality rates based on any mention of a disease. Approaches used to assess measures of associations between diseases include the Cause-of-Death Association Indicator (CDAI) and social network analysis. The advanced statistical methods include weighting MCoD and lethal defect-wear model of mortality. Audit results will be discussed. Conclusions This review provides a comprehensive and updated summary of methodological approaches used to analyse MCoD data. The merit of each analytical framework is discussed. Key messages More work is needed to develop methodological frameworks that could be used to support routine consideration of MCoD in practice.

scholarly journals A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation

10.2196/17125 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e17125 ◽  
Author(s):  
Louis Falissard ◽  
Claire Morgand ◽  
Sylvie Roussel ◽  
Claire Imbaud ◽  
Walid Ghosn ◽  
...  
Keyword(s):  
Neural Network ◽  
Cause Of Death ◽  
Deep Neural Network ◽  
Causes Of Death ◽  
State Of The Art ◽  
Death Certificates ◽  
Current State ◽  
Underlying Causes ◽  
Artificial Neural ◽  
Underlying Cause Of Death

Background Coding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d’épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. Objective This article investigates the application of deep neural network methods to coding underlying causes of death. Methods The investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject’s age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject’s underlying cause of death was then formulated as a predictive modelling problem. A deep neural network−based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach’s superiority was assessed via bootstrap. Results The proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. Conclusions This article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.

scholarly journals Four Decades of COPD Mortality Trends: Analysis of Trends and Multiple Causes of Death

2021 ◽  
Vol 10 (5) ◽  
pp. 1117
Author(s):  
Alberto Fernández-García ◽  
Mónica Pérez-Ríos ◽  
Alberto Fernández-Villar ◽  
Gael Naveira ◽  
Cristina Candal-Pedreira ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Causes Of Death ◽  
Chronic Obstructive ◽  
Joinpoint Regression ◽  
Mortality Trend ◽  
Mortality Trends ◽  
Obstructive Pulmonary Disease ◽  
Underlying Cause Of Death ◽  
Copd Mortality ◽  
Age Of Death

There is little information on chronic obstructive pulmonary disease (COPD) mortality trends, age of death, or male:female ratio. This study therefore sought to analyze time trends in mortality with COPD recorded as the underlying cause of death from 1980 through 2017, and with COPD recorded other than as the underlying cause of death. We conducted an analysis of COPD deaths in Galicia (Spain) from 1980 through 2017, including those in which COPD was recorded other than as the underlying cause of death from 2015 through 2017. We calculated the crude and standardized rates, and analyzed mortality trends using joinpoint regression models. There were 43,234 COPD deaths, with a male:female ratio of 2.4. Median age of death was 82 years. A change point in the mortality trend was detected in 1996 with a significant decrease across the sexes, reflected by an annual percentage change of −3.8%. Taking deaths into account in which COPD participated or contributed without being the underlying cause led to an overall 42% increase in the mortality burden. The most frequent causes of death when COPD was not considered to be the underlying cause were bronchopulmonary neoplasms and cardiovascular diseases. COPD mortality has decreased steadily across the sexes in Galicia since 1996, and age of death has also gradually increased. Multiple-cause death analysis may help prevent the underestimation of COPD mortality.

Comparing Underlying and Contributory Cause of Death in Registry Data With End-of-Life Proxy Interviews: Findings From The Irish Longitudinal Study on Ageing (TILDA)

2020 ◽  
pp. 073346482093529
Author(s):  
Mark Ward ◽  
Peter May ◽  
Charles Normand ◽  
Rose Anne Kenny ◽  
Anne Nolan
Keyword(s):  
Longitudinal Study ◽  
End Of Life ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Community Dwelling ◽  
Death Certificates ◽  
Nationally Representative ◽  
Underlying Cause Of Death ◽  
Kappa Agreement ◽  
Level Of Agreement

Cause of death is an important outcome in end-of-life (EOL) research. However, difficulties in assigning cause of death have been well documented. We compared causes of death in national death registrations with those reported in EOL interviews. Data were from The Irish Longitudinal Study on Ageing (TILDA), a nationally representative sample of community-dwelling adults aged 50 years and older. The kappa agreement statistic was estimated to assess the level of agreement between two methods: cause of death reported in EOL interviews and those recorded in official death registrations. There was moderate agreement between underlying cause of death recorded on death certificates and those reported in EOL interviews. Discrepancies in reporting in EOL interviews were systematic with better agreement found among younger decedents and where the EOL informant was the decedents’ partner/spouse. We have shown that EOL interviews may have limited utility if the main goal is to understand the predictors and antecedents of different causes of death.

scholarly journals Comparing identification of children and young people with a life limiting condition from cause of death with use of an ICD-10 coding framework applied to routine clinical data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Stuart Jarvis ◽  
Lorna Fraser
Keyword(s):  
Young People ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Age Groups ◽  
Survival Times ◽  
Children And Young People ◽  
Icd 10 ◽  
Underlying Cause Of Death ◽  
Birth Records ◽  
The Individual

ABSTRACTObjectivesTo compare methods of estimating prevalence of life limiting conditions (LLC) among children and young people (CYP) using (i) cause of death recorded on death certificates and (ii) diagnostic codes in routinely collected inpatient and birth records. ApproachCYP with a LLC were identified from NHS inpatient and birth records in Scotland from 1 April 2003 to 30 March 2014 using a LLC ICD-10 coding framework. The cohort was restricted to individuals who died in the study period. For each cohort member, the LLC coding framework was used to determine whether a diagnosis identified as a LLC was recorded as the underlying cause of death. For those without LLC as an underlying cause of death, the underlying cause was checked to determine whether it was related to LLC – either itself indicative of LLC when recorded on a death certificate or related to one or more of the LLCs identified in the individual’s inpatient and birth records. Finally, for those with underlying cause of death neither a LLC nor related to a LLC, the contributing causes of death were checked against the coding framework for LLC; where found, the individual was marked as having a LLC as a contributing cause of death. These analyses were undertaken for the whole cohort, per year, by age groups and by diagnostic categories. Results20436 CYP with a LLC were identified between 1 April 2009 and 31 March 2014, of which 2249 had died and had a death register record. Of these, 1291 (57%) had a LLC as underlying cause of death; 319 (14%) had an LLC-related underlying cause of death and 268 (12%) had LLC only among contributing causes of death. 371 (16%) had no indication of LLC in their death records. Recording of a LLC as underlying cause of death was lower (41%) amongst under 1 year olds and also varied widely by diagnostic group. ConclusionAround one in six of CYP identified using the coding framework as having a LLC (and almost one in five of under 1s) would not have been so identified using all causes of death in death records. More than a quarter (28%) would be missed if only underlying cause of death was used. This, combined with longer survival times, means use of death records has the potential to greatly underestimate prevalence of LLC in children and young people.

Abstract P059: Multiple Cause-of-Death Analysis Reveals Under Reporting of Disease Burden for Diabetes

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Katherine G Hastings ◽  
Jiaqi Hu ◽  
Nadejda Marques ◽  
Eric J Daza ◽  
Mark Cullen ◽  
...  
Keyword(s):  
Ethnic Groups ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Mortality Rates ◽  
Minority Populations ◽  
Race Ethnicity ◽  
Underlying Cause Of Death ◽  
The Impact ◽  
Racial Ethnic ◽  
The U.S

Introduction: Despite being considerably under reported as the underlying cause of death on death certificates, and consequently on mortality figures, diabetes is among the ten leading causes of death in the U.S. A multiple cause-of-death analysis shows the extent to which diabetes is associated with other leading causes of death. Hypothesis: Analysis of multiple-cause-of-death will confirm prevalence rates of diabetes among racial/ethnic minority populations, demonstrate the impact of diabetes in association with other causes of death, and highlight variations of burden of disease among different racial/ethnic groups. Methods: Causes of death were identified using the Multiple Cause Mortality Files of the National Center for Health Statistics from 2003 to 2012. Age-adjusted mortality rates were calculated for diabetes both as the underlying cause of death (UCD) and as multiple causes of death (MCD) by racial/ethnic groups (NHWs, Blacks, Asians, and Hispanic/Latinos). Frequencies and proportions were calculated by race/ethnicity groups. Linear regression model was used for number of causes per death. Results: A total of 2,335,198 decedents had diabetes listed as MCD in the U.S. national death records from 2003-2012. Mortality rates of diabetes as MCD were 3.4 times than UCD for Asians, 2.9 times for Blacks, 2.9 times for Hispanics and 3.7 times for NHWs (Figure). Minority populations had higher proportion of deaths with diabetes reported as MCD than NHWs (1.7 times higher for Hispanics, 1.5 times higher for Blacks and Asians). Adjusting for age, gender, and race/ethnicity, there were 1.7 more causes per death co-occurred for diabetes decedents compared to decedents who died due to all other causes (95% CI: 1.714, 1.718). Conclusions: Our findings underscore the importance of a multiple-cause-of-death approach in the analyses for a more comprehensive understanding of the impact of diabetes.

Causes of Death in People with Dementia from 2002 to 2015: A Nationwide Study

2021 ◽  
pp. 1-10
Author(s):  
Lærke Taudorf ◽  
Ane Nørgaard ◽  
Sabrina Islamoska ◽  
Thomas Munk Laursen ◽  
Gunhild Waldemar
Keyword(s):  
Cause Of Death ◽  
Elderly Population ◽  
Causes Of Death ◽  
People With Dementia ◽  
Underlying Causes ◽  
Annual Frequency ◽  
Marked Decline ◽  
Underlying Cause Of Death ◽  
General Elderly Population ◽  
Over Time

Background: Dementia is associated with increased mortality. However, it is not clear whether causes of death in people with dementia have changed over time. Objective: To investigate if causes of death changed over time in people with dementia compared to the general elderly population. Methods: We included longitudinal data from nationwide registries on all Danish residents aged≥65 years to 110 years who died between 2002 to 2015. We assessed the annual frequency of dementia-related deaths (defined as a dementia diagnosis registered as a cause of death) and of underlying causes of death in people registered with dementia compared to the general elderly population. Results: From 2002 to 2015, 621,826 people died, of whom 103,785 were diagnosed with dementia. During this period, the percentage of dementia-related deaths increased from 10.1%to 15.2%in women, and from 6.3%to 9.5%in men in the general elderly population. From 2002 to 2015, dementia became the leading, registered underlying cause of death in people with diagnosed dementia. Simultaneously, a marked decline in cardiovascular and cerebrovascular deaths was observed in people with and without dementia. Conclusion: This is the first study to investigate if the causes of death change over time in people with diagnosed dementia compared with the general elderly population. The increase in the registration of dementia as an underlying cause of death could reflect increasing awareness of dementia as a fatal condition.

scholarly journals An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens

2021 ◽  
Vol 18 (5) ◽  
pp. 2414
Author(s):  
Hui Ge ◽  
Keyan Gao ◽  
Shaoqiong Li ◽  
Wei Wang ◽  
Qiang Chen ◽  
...  
Keyword(s):  
Health Status ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Health Policies ◽  
Automatic Method ◽  
Comprehensive Understanding ◽  
Public Health Policies ◽  
Underlying Causes ◽  
Manual Methods ◽  
Underlying Cause Of Death

It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes-of-death for citizens. A sink algorithm is introduced, which could perform automatic inference of the underlying cause-of-death for citizens. The results show that our sink algorithm could generate a reasonable output and outperforms other stat-of-the-art algorithms. We believe it would be very useful to greatly enhance the efficiency of correct inferences of the underlying causes-of-death for citizens.

scholarly journals O6A.6 Mortality experience of in women in south african mining: 2013 – 2015

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A52.2-A52
Author(s):  
Kerry Wilson ◽  
Tahira Kootbodien ◽  
Nisha Naicker
Keyword(s):  
South Africa ◽  
Cause Of Death ◽  
Causes Of Death ◽  
Smoking Status ◽  
Multiple Logistic Regression Analysis ◽  
Registration Data ◽  
Vital Registration Data ◽  
External Causes Of Death ◽  
Underlying Cause Of Death ◽  
External Causes

Mining is a high-risk industry with both continued accidents and occupational disease, despite controls introduced in the industry. In this study, we looked at the sex differences in mortality between male and female miners in South Africa.MethodsThe use of vital registration data for monitoring mortality in miners has largely been unexplored in South Africa. Statistics South Africa provides data from 2013 to 2015 which was used in students-t-tests along with proportion tests to investigate differences between death in all women and women miners along with differences in deaths in male miners and women miners. Multiple logistic regression analysis was performed to calculate mortality odds ratios (MORs) for the underlying cause of death in these groups, with adjustments for age, education level, province of death and smoking status.ResultsOf the 8769 deaths recorded with occupation miner ion the years 2013–2015, only 5.7% were in females. Significant differences between all women and women miners were found in age at death (58.8 vs 47.8), no 1 cause of death (ill-defined vs TB) and education (43.6% vs 63.6%). MORs were significantly increased in women miners for TB, HIV and external causes of death compared to all women while being protected from lifestyle and chronic diseases. Women miners compared to male miners had increased odds of HIV death and lifestyle diseases but a similar risk of external causes of death.DiscussionWomen miners appear to die at significantly younger ages than both male miners and other women despite a higher level of education. This may be due to the increased mortality due to HIV and external causes of death. Thus increased controls are required on mines to protect the health of women miners.

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