P105 Population mortality rates, case fatality ratios and the reconfiguration of services: regional and longitudinal variation in Ireland 2002–2012

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.

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

Population Health Metrics ◽

10.1186/s12963-022-00282-7 ◽

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.

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United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status

Healthcare ◽

10.3390/healthcare8030330 ◽

2020 ◽

Vol 8 (3) ◽

pp. 330

Author(s):

Rashid Ahmed ◽

Mark Williamson ◽

Muhammad Akhter Hamid ◽

Naila Ashraf

Keyword(s):

United States ◽

Case Fatality ◽

The United States ◽

Mortality Rates ◽

Small Sample ◽

County Level ◽

Death Rates ◽

Rural Counties ◽

Urban Classification ◽

Population Mortality

COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.

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

10.1101/2021.01.24.21250411 ◽

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.

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Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

The Innovation ◽

10.1016/j.xinn.2020.100047 ◽

2020 ◽

Vol 1 (3) ◽

pp. 100047 ◽

Cited By ~ 7

Author(s):

Donghai Liang ◽

Liuhua Shi ◽

Jingxuan Zhao ◽

Pengfei Liu ◽

Jeremy A. Sarnat ◽

...

Keyword(s):

United States ◽

Air Pollution ◽

Urban Air Pollution ◽

Case Fatality ◽

The United States ◽

Mortality Rates ◽

Urban Air

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On Dynamics of Gender-Specific Mortality Among the Population of the Republic of Belarus in the Second Half of the XX – the Beginning of the XXI Centuries

Galician Medical Journal ◽

10.21802/gmj.2017.3.2 ◽

2017 ◽

Vol 24 (3) ◽

Author(s):

A Romanova ◽

O Krasko

Keyword(s):

Mortality Rate ◽

Environmental Changes ◽

Research Work ◽

Mortality Rates ◽

Female Mortality ◽

Female Population ◽

Male And Female ◽

Crude Mortality ◽

The Republic ◽

Population Mortality

Aim of the study: to evaluate the dynamics and to make a comparative analysis of male and female mortality among the population of Belarus Republic during 1959 – 2015.Materials and methods. The data on natural population movement in the Republic of Belarus during 1959 – 2015 have been analyzed in the research work. Crude and standardized mortality rates have been calculated using the direct standardization according to the world standard (Standard “World”), approved by WHO. JoinPoint software was used to investigate time trends as well as office suite MSEXCEL 2010.Results of the study. The minimum values of male and female crude and standardized mortality rates were established in 1964. Throughout the study period, the male population mortality rate grew 1.8-fold (based on crude rates – 2.4-fold), the female population mortality rate – 1.6-fold (based on crude rates – 2.2-fold). During 1985 – 2005, the differences in crude mortality rates among men and women grew 1.2-fold, and during 1962 – 2011, the differences in standardized rates increased 1.8-fold. Since 2003, the mortality rate among men and since 1999, the death rate among women has declined with an annual decrease rate to be more than twice as high as compared to an annual mortality increase registered during its growth.Conclusion. Since the 1960s, the changes in population age structure of the male and female population affected the crude mortality rates. The male and female mortality growth is due to an increased unfavorable impact of combined environmental factors. The adaptive capacity of women to sustain environmental changes contributed to their later entry into the period of mortality growth, as compared to men. The mortality rate reduction in men since 2003 and the excess of a decrease over an increase rate is associated with a set of state measures aimed at protecting and strengthening the public health in the republic.

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Correction: Twenty-five-year trends in myocardial infarction attack and mortality rates, and case-fatality, in six European populations

Heart ◽

10.1136/heartjnl-2014-307310corr1 ◽

2018 ◽

Vol 104 (16) ◽

pp. e2.1-e2

Keyword(s):

Myocardial Infarction ◽

Case Fatality ◽

Mortality Rates ◽

European Populations

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Risk of death among people with rare autoimmune diseases compared to the general population in England during the 2020 COVID-19 pandemic

Rheumatology ◽

10.1093/rheumatology/keaa855 ◽

2020 ◽

Author(s):

Emily Peach ◽

Megan Rutter ◽

Peter Lanyon ◽

Matthew J Grainge ◽

Richard Hubbard ◽

...

Keyword(s):

General Population ◽

Healthcare Services ◽

Mortality Rates ◽

Published Data ◽

Risk Of Death ◽

Increased Risk ◽

Specific Mortality ◽

Population Mortality ◽

The Uk ◽

Recorded Diagnosis

Abstract Objectives To quantify the risk of death among people with rare autoimmune rheumatic diseases (RAIRD) during the UK 2020 COVID-19 pandemic compared with the general population, and compared with their pre-COVID risk. Methods We conducted a cohort study in Hospital Episode Statistics for England 2003 onwards, and linked data from the NHS Personal Demographics Service. We used ONS published data for general population mortality rates. Results We included 168 691 people with a recorded diagnosis of RAIRD alive on 01/03/2020. Their median age was 61.7 (IQR 41.5–75.4) years, and 118 379 (70.2%) were female. Our case ascertainment methods had a positive predictive value of 85%. 1,815 (1.1%) participants died during March and April 2020. The age-standardised mortality rate (ASMR) among people with RAIRD (3669.3, 95% CI 3500.4–3838.1 per 100 000 person-years) was 1.44 (95% CI 1.42–1.45) times higher than the average ASMR during the same months of the previous 5 years, whereas in the general population of England it was 1.38 times higher. Age-specific mortality rates in people with RAIRD compared with the pre-COVID rates were higher from the age of 35 upwards, whereas in the general population the increased risk began from age 55 upwards. Women had a greater increase in mortality rates during COVID-19 compared with men. Conclusion The risk of all-cause death is more prominently raised during COVID-19 among people with RAIRD than among the general population. We urgently need to quantify how much risk is due to COVID-19 infection and how much is due to disruption to healthcare services.

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