scholarly journals COVID-19 mortality risk for older men and women

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
N. David Yanez ◽  
Noel S. Weiss ◽  
Jacques-André Romand ◽  
Miriam M. Treggiari
Keyword(s):  
Ecological Study ◽  
Total Population ◽  
Case Fatality ◽  
Population Level ◽  
Mortality Rates ◽  
Mixed Effects ◽  
Age Group ◽  
Incident Rate Ratio ◽  
Incident Rate ◽  
Older Men And Women

Abstract Background Case-fatality from COVID-19 has been reported to be relatively high in patients age 65 years or older. We sought to determine the age-specific rates of COVID-19 mortality at the population level. Methods We obtained information regarding the total number of COVID-19 reported deaths for six consecutive weeks beginning at the 50th recorded death, among 16 countries that reported a relatively high number of COVID-19 cases as of April 12, 2020. We performed an ecological study to model COVID-19 mortality rates per week by age group (54 years or younger, 55–64 years, and 65 years or older) and sex using a Poisson mixed effects regression model. Results Over the six-week period of data, there were 178,568 COVID-19 deaths from a total population of approximately 2.4 billion people. Age and sex were associated with COVID-19 mortality. Compared with individuals ages 54 years or younger, the incident rate ratio (IRR) was 8.1, indicating that the mortality rate of COVID-19 was 8.1 times higher (95%CI = 7.7, 8.5) among those 55 to 64 years, and more than 62 times higher (IRR = 62.1; 95%CI = 59.7, 64.7) among those ages 65 or older. Mortality rates from COVID-19 were 77% higher in men than in women (IRR = 1.77, 95%CI = 1.74, 1.79). Conclusions In the 16 countries examined, persons age 65 years or older had strikingly higher COVID-19 mortality rates compared to younger individuals, and men had a higher risk of COVID-19 death than women.

Description of Total Population Hospital Admissions for Morton's Metatarsalgia in Australia

2014 ◽  
Vol 104 (5) ◽  
pp. 451-454 ◽  
Author(s):  
Reza Naraghi ◽  
Alan Bryant ◽  
Linda Slack-Smith
Keyword(s):  
Old Age ◽  
Total Population ◽  
Hospital Admissions ◽  
Admission Rate ◽  
Population Level ◽  
Age Group ◽  
Explanatory Variables ◽  
Icd 10 ◽  
Morton’S Metatarsalgia ◽  
Morton's Metatarsalgia

Background Morton's metatarsalgia is a painful perineural fibroma of a plantar nerve, most commonly of the second or third intermetatarsal spaces of the forefoot. The aim of this study was to investigate hospital admissions with a diagnosis of Morton's metatarsalgia in the Australian population from 1998 to 2008. Methods Data regarding admissions with a diagnosis code of ICD-10 G57.6 were extracted from the Australian Institute of Health and Welfare databases of hospital morbidity from 1998 to 2008. The event of interest was an admission with ICD-10 G57.6 (Morton's metatarsalgia). The explanatory variables included sex and age group. Rates were calculated using the estimated resident population counts to determine denominators. Results Morton's metatarsalgia admissions were almost three-fold higher for women in the population compared to men. The rate of admissions for Morton's metatarsalgia was the highest for the total population in the 55- to 59-year-old age group. Among women admitted for Morton's metatarsalgia, the highest rate was in the 50- to 54-year-old age group; among men, the highest rate was in the slightly older 55- to 59-year-old age category. Conclusions Population-level information on admissions for Morton's metatarsalgia show that admissions were three times higher among women compared to men. The highest admission rate was in the 50- to 55-year-old age group.

scholarly journals Varicella in non-immune persons: incidence, hospitalization and mortality rates

2002 ◽  
Vol 129 (3) ◽  
pp. 599-606 ◽  
Author(s):  
P. Y. BOËLLE ◽  
T. HANSLIK
Keyword(s):  
Case Fatality ◽  
Mortality Rates ◽  
Morbidity And Mortality ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Age Group ◽  
Death Rates ◽  
Hospitalization Rates ◽  
Incidence Prevalence

This study was conducted to estimate the varicella morbidity and mortality rates per age group among the non-immune population in France. Morbidity and mortality data for the years 1990–9 were derived from nationwide databases and surveillance systems. An incidence/prevalence model was designed to quantify the non-immune population per age group. The incidence of varicella in the non-immune population peaks during childhood and again in the 25–35 years age group. For children aged 1–4 years, adults aged 25–34 years and those older than 65 years, the hospitalization rates are respectively 235, 1438 and 8154 per 100 000 cases, and the death rates are respectively 7, 104 and 5345 per million cases. Case fatality or case hospitalization rates were not evenly distributed among adults and increased dramatically with age.

scholarly journals Disaggregating Proportional Multistate Lifetables by Population Heterogeneity to Estimate Intervention Impacts on Inequalities

2021 ◽  
Author(s):  
Patrick Andersen ◽  
Anja Mizdrak ◽  
Nick Wilson ◽  
Anna Davies ◽  
Laxman Bablani ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Total Population ◽  
Case Fatality ◽  
Mortality Rates ◽  
Time Step ◽  
Life Years ◽  
All Cause Mortality ◽  
Population Mortality ◽  
Rate Ratios

Abstract BackgroundSimulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities.We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. MethodsWe developed a disaggregation algorithm that iteratively rescales mortality, incidence and case fatality rates by time-step of the model to ensure correct total population counts were retained at each step.To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups.The three interventions were then run on top of these scaled BAU scenarios. ResultsThe algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (health adjusted life years) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population.ConclusionPolicy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

scholarly journals Determinants of the decline in mortality from acute stroke in England: linked national database study of 795 869 adults

BMJ ◽  
2019 ◽  
pp. l1778 ◽  
Author(s):  
Olena O Seminog ◽  
Peter Scarborough ◽  
F Lucy Wright ◽  
Mike Rayner ◽  
Michael J Goldacre
Keyword(s):  
Acute Stroke ◽  
Event Rate ◽  
Age Groups ◽  
Case Fatality ◽  
Mortality Rates ◽  
National Database ◽  
Age Group ◽  
Stroke Mortality ◽  
Stroke Event ◽  
Event Rates

Abstract Objectives To study trends in stroke mortality rates, event rates, and case fatality, and to explain the extent to which the reduction in stroke mortality rates was influenced by changes in stroke event rates or case fatality. Design Population based study. Setting Person linked routine hospital and mortality data, England. Participants 795 869 adults aged 20 and older who were admitted to hospital with acute stroke or died from stroke. Main outcome measures Stroke mortality rates, stroke event rates (stroke admission or stroke death without admission), and case fatality within 30 days after stroke. Results Between 2001 and 2010 stroke mortality rates decreased by 55%, stroke event rates by 20%, and case fatality by 40%. The study population included 358 599 (45%) men and 437 270 (55%) women. Average annual change in mortality rate was −6.0% (95% confidence interval −6.2% to −5.8%) in men and −6.1% (−6.3% to −6.0%) in women, in stroke event rate was −1.3% (−1.4% to −1.2%) in men and −2.1% (−2.2 to −2.0) in women, and in case fatality was −4.7% (−4.9% to −4.5%) in men and −4.4% (−4.5% to −4.2%) in women. Mortality and case fatality but not event rate declined in all age groups: the stroke event rate decreased in older people but increased by 2% each year in adults aged 35 to 54 years. Of the total decline in mortality rates, 71% was attributed to the decline in case fatality (78% in men and 66% in women) and the remainder to the reduction in stroke event rates. The contribution of the two factors varied between age groups. Whereas the reduction in mortality rates in people younger than 55 years was due to the reduction in case fatality, in the oldest age group (≥85 years) reductions in case fatality and event rates contributed nearly equally. Conclusions Declines in case fatality, probably driven by improvements in stroke care, contributed more than declines in event rates to the overall reduction in stroke mortality. Mortality reduction in men and women younger than 55 was solely a result of a decrease in case fatality, whereas stroke event rates increased in the age group 35 to 54 years. The increase in stroke event rates in young adults is a concern. This suggests that stroke prevention needs to be strengthened to reduce the occurrence of stroke in people younger than 55 years.

scholarly journals Morbidity and Mortality of Traumatic Brain Injury in Hospitalized Patients in Brazil Between 2010 and 2020: An Ecological Study

2021 ◽  
Author(s):  
Vitória Bittencourt de Carvalho ◽  
Kauan Alves Sousa Madruga
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mortality Rate ◽  
Ecological Study ◽  
Traumatic Injury ◽  
Mortality Rates ◽  
Hospitalization Rate ◽  
Morbidity And Mortality ◽  
Age Group ◽  
Unified Health System

Background: Traumatic Brain Injury (TBI) is defined as any traumatic injury causing an anatomical lesion or functional impairment of the scalp, skull, meninges, brain or its vessels. Hospitalization of this patient, depending on the severity, can result in irreversible sequelae or death. Objective: To report the morbidity and mortality rates of patients suffering from TBI hospitalized in Brazilian hospitals between 2010 and 2020. Methods: Descriptive ecological study of the data collected at the Informatics Department of the Unified Health System (DATASUS). Results: There were 1,143,187 admissions due to TBI. There was a predominance of males with 871,999 (76.28%) cases and the age group between 20 and 29 years old 199,857 (17.48%). Brown patients were the ones with the highest hospitalization rate: 370,639 (32.42%). The mortality rate in the period was 9.52/100 hospitalizations, with the Southeast region occupying the first place (10.44 per 100 hospitalizations). In total, 108,853 deaths were recorded, of which 50,013 occurred in the Southeast, the region with the highest rate. Although the number of deaths was higher in people between 20 and 29 years old (16,687), the age group with the highest mortality rate was over 80 years old (19.84 per 100 hospitalizations). Conclusion: In the last 10 years, TBI has caused 1,143,187 hospitalizations in Brazil, with a predominance of males and the age group between 20 and 29 years. Brown patients had the highest rate of hospitalization. The region with the highest mortality was the Southeast and the smallest was the South.

scholarly journals Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Patrick Andersen ◽  
Anja Mizdrak ◽  
Nick Wilson ◽  
Anna Davies ◽  
Laxman Bablani ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Total Population ◽  
Case Fatality ◽  
Simulation Models ◽  
Mortality Rates ◽  
Time Step ◽  
Life Years ◽  
All Cause Mortality ◽  
Population Mortality ◽  
Rate Ratios

Abstract Background Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. Methods We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios. Results The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population. Conclusion Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

scholarly journals Inequalities in Mortality and Access to Hospital Care for Cervical Cancer—An Ecological Study

2021 ◽  
Vol 18 (20) ◽  
pp. 10966
Author(s):  
Luiz Vinicius de Alcantara Sousa ◽  
Erika da Silva Maciel ◽  
Laércio da Silva da Silva Paiva ◽  
Stefanie de Sousa Antunes Alcantara ◽  
Vânia Barbosa do Nascimento ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Hospital Admission ◽  
Ecological Study ◽  
Age Groups ◽  
The United States ◽  
Mortality Rates ◽  
Age Group ◽  
The South ◽  
The North ◽  
Hospitalization Rates

Cervical cancer is the second most common form of cancer in the world among women, and it is estimated to be the third most frequent cancer in Brazil, as well as the fourth leading cause of death from cancer. There is a difference in cervical cancer mortality rates among different administrative regions in Brazil along with an inadequate distribution of cancer centers in certain Brazilian regions. Herein, we analyze the trends in hospital admission and mortality rates for CC between 2000 and 2012. This population-based ecological study evaluated the temporal trend in cervical cancer between the years 2000 and 2012, stratifying by Brazilian administrative regions. The North and Northeast regions had no reduction in mortality in all age groups studied (25 to 64 years); when analyzing hospitalization rates, only the age group of 50 to 64 years from the North Region did not present a reduction. During the years studied, in the South Region, the age group ranging from 50 to 54 years had the greatest reduction in mortality rates (β = −0.59, p = 0.001, r2 = 0.63), and the group ranging from 45 to 49 years had the greatest reduction in hospital admission rates (β = −8.87, p = 0.025, r2 = 0.37). Between the years 2000 and 2012, the greatest reduction in the incidence of UCC was in the South Region (β = −1.43, p = 0.236, r2 = 0.12) followed by the Central-West (β = −1, p <0.001, r2 = 0.84), the Southeast (β = −0.95, p <0.001, r2 = 0.88), the Northeast (β = −0.67, p = 0.080, r2 = 0.25), and, finally, by the North (β = −0.42, p = 0.157, r2 = 0.17). There was a greater reduction in mortality rates and global hospitalization rates for CC in Brazil than in the United States during the same period with exceptions only in Brazil’s North and Northeast regions.

scholarly journals Clinical and epidemiological variability in severe sepsis: an ecological study

2018 ◽  
Vol 72 (8) ◽  
pp. 741-745 ◽  
Author(s):  
J Priyanka Vakkalanka ◽  
Karisa K Harland ◽  
Morgan B Swanson ◽  
Nicholas M Mohr
Keyword(s):  
Severe Sepsis ◽  
Case Fatality Rate ◽  
Ecological Study ◽  
Case Fatality ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Administrative Database ◽  
Fatality Rate ◽  
Diagnostic Coding ◽  
Hospital Administrative

BackgroundTo assess clinical and epidemiological trends of severe sepsis.MethodsEcological study of patients presenting to the emergency department with severe sepsis or septic shock between 2005 and 2013. Patients were identified using the state-wide hospital administrative database. Key outcomes included incidence rates (IRs) and mortality rates (per 1000 population) by age and medically underserved areas (MUAs), sepsis case fatality rate (deaths per 100 sepsis cases), and proportions of transfer and comorbidities.ResultsThere were 154 019 sepsis cases identified. In 2005, 85+ yo in non-MUAs had a 44% increase in IR compared with those in MUAs, and this difference rose to 74% by 2013. Mortality rates were 1.6 (95% CI 1.3 to 1.8) times greater among 85+ yo in non-MUAs. Mortality rates increased by 1.8% annually, while the sepsis case fatality rate decreased by 7.7%. The proportion of transfer among sepsis cases decreased by 2.1% per year (3.8% in non-MUAs, 0.7% in MUAs).ConclusionsSepsis incidence varies geographically, and access to healthcare is one proposed mechanism that may explain heterogeneity. Over time, we may be capturing higher acuity sepsis cases with better recognition and management, as well as observing differential diagnostic coding documentation by location.

scholarly journals Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

2021 ◽  
Author(s):  
Patrick Andersen ◽  
Anja Mizdrak ◽  
Nick Wilson ◽  
Anna Davies ◽  
Laxman Bablani ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Total Population ◽  
Case Fatality ◽  
Simulation Models ◽  
Mortality Rates ◽  
Time Step ◽  
Life Years ◽  
All Cause Mortality ◽  
Population Mortality ◽  
Rate Ratios

AbstractBackgroundSimulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities.We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply.MethodsWe developed a disaggregation algorithm that iteratively rescales mortality, incidence and case fatality rates by time-step of the model to ensure correct total population counts were retained at each step.To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality &morbidity rates, coronary heart disease incidence &case fatality rates; stroke incidence &case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups.The three interventions were then run on top of these scaled BAU scenarios.ResultsThe algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (health adjusted life years) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population.ConclusionPolicy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

DISTRIBUTION AND CHARACTERISTICS OF HEAD INJURY AND REFERRAL NUMBER AT DR H. ANDI ABDURRAHMAN NOOR GENERAL HOSPITAL, TANAH BUMBU, SOUTH BORNEO, INDONESIA

2019 ◽  
Vol 1 (4) ◽  
Author(s):  
Komang Werdhi Sentosa ◽  
Ani Rahmawati ◽  
Daldy Arianda ◽  
Ardik Lahdimawan ◽  
Agus Suhendar
Keyword(s):  
Head Injury ◽  
General Hospital ◽  
Sampling Method ◽  
Total Population ◽  
Head Injuries ◽  
Age Group ◽  
Population Sampling ◽  
Injury Patient ◽  
Incident Rate ◽  
One Year

Background: Head injury (HI) has been one among leading causes of morbidity and mortality worldwide especially in the peripheries area. In South Borneo, 9.4% of trauma cases was a head injury. Especially, Tanah Bumbu Regency, one of peripheries area in South Borneo ranks third for head injuries after Tabalong and Tanah Laut Regency in 2007.Objective: The aim of this study was to describe the characteristics of head injury patient and referral number at Dr. H. Andi Abdurrahman Noor general hospital.Methods: All head injury patients admitted to the emergency department (ED) of Dr. H. Andi Abdurrahman Noor general Hospital in a one-year period (2017) were registered in this retrospective study. Using the total population sampling method, 413 cases of head injury during the period were included as a subject of study.Result: This study showed that mild head injury was the most cases of head injury with 325 cases (78.2%). 61 patients were referred to a higher trauma center in 2017. Head injury was most common in 11-20 years old age group. Men also had higher incident rate compared to women (2:1). Most of the patients were a nonstate employee. Head injury is commonly caused by traffic accident.Conclusion: This study shows that characteristics of HI in the peripheries area such Tanah Bumbu regency are no different from other countries. Our findings suggest that several prevention steps should be taken to reduce the number of head injury based on the distribution and characteristics of head injury sustainers.

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