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 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.

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 Excess Mortality Attributable to COVID-19 Among Assisted Living Residents

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 58-58
Author(s):  
Kali Thomas ◽  
Wenhan Zhang ◽  
David Dosa ◽  
Paula Carder ◽  
Philip Sloane ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Confidence Intervals ◽  
Assisted Living ◽  
Excess Mortality ◽  
Mortality Rates ◽  
All Cause Mortality ◽  
National Cohort ◽  
Adjusted Incidence Rate ◽  
Rate Ratios ◽  
Adjusted Incidence

Abstract This study examines the excess mortality attributable to COVID-19 among a national cohort of assisted living (AL) residents. To do this, we compare the weekly rate of all-cause mortality during 1/1/20-8/11/20 with the same weeks in 2019 and calculated adjusted incidence rate ratios (IRRs) and 95% confidence intervals (CIs). All-cause mortality rates, nationally, were 14% higher in 2020 compared with 2019 (mean, 2.309 vs. 2.020, respectively, per 1000 residents per week; adjusted IRR, 1.169; 95% CI 1.165-1.173). Among the 10 states with the highest community spread, the excess mortality attributable to COVID-19 was 24% higher, with 2.388 deaths per 1000 residents per week in 2020 during January-August vs 1.928 in 2019 (adjusted IRR, 1.241; 95% CI 1.233-1.250). These results suggest that AL residents suffered excess mortality due to COVID-19.

scholarly journals Mortality in Norway and Sweden before and after the Covid-19 outbreak: a cohort study

2020 ◽  
Author(s):  
Frederik E Juul ◽  
Henriette C Jodal ◽  
Ishita Barua ◽  
Erle Refsum ◽  
Ørjan Olsvik ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Mortality Rates ◽  
List Type ◽  
School Closures ◽  
Epidemic Period ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Before And After ◽  
Rate Ratios ◽  
Expected Mortality

AbstractObjectivesNorway and Sweden are similar countries regarding ethnicity, socioeconomics and health care. To combat Covid-19, Norway implemented extensive measures such as school closures and lock-downs, while Sweden has been criticised for relaxed measures against Covid-19. We compared the effect of the different national strategies on all-cause and Covid-19 associated mortality.DesignRetrospective cohort.SettingThe countries Norway and Sweden.ParticipantsAll inhabitants.Main outcome measuresWe calculated weekly mortality rates (MR) with 95% confidence intervals (CI) per 100,000 individuals as well as mortality rate ratios (MRR) comparing the epidemic year (29th July, 2019 to 26th July, 2020) to the four preceding years (July 2015 to July 2019). We also compared Covid-19 associated deaths and mortality rates for the weeks of the epidemic in Norway and Sweden (16th March to 26th July, 2020).ResultsIn Norway, mortality rates were stable during the first three 12-month periods of 2015/16; 2016/17 and 2017/18 (MR 14.8 to 15.1 per 100,000), and slightly lower in the two most recent periods including during epidemic period (2018/19 and 2019/20; 14.5 per 100,000). In Sweden, all-cause mortality was stable during the first three 12-month periods of 2015/16; 2016/17 and 2017/18 (MR 17.2 to 17.5 per 100,000), but lower in the year 2018/19 immediately preceding the epidemic (16.2 per 100,000). Covid-19 associated mortality rates were 0.2 per 100,000 (95%CI 0.1 to 0.4) in Norway and 2.9 (95%CI 1.9 to 3.9) in Sweden. The increase in mortality was confined to individuals in 70 years or older.ConclusionsAll-cause mortality remained unaltered in Norway. In Sweden, the observed increase in all-cause mortality during Covid-19 was partly due to a lower than expected mortality preceding the epidemic and the observed excess mortality, was followed by a lower than expected mortality after the first Covid-19 wave. This may suggest mortality displacement.Strengths and limitations of this studyCompares two similar contries in all aspects but the handling of the Covid-19 epidemicEvaluates the mortality for several years before and during the epidemicProvides a possible explanation of the observed mortality changesDiscusses the socioeconomic effects of the different strategies in the two countriesDoes not evaluate cause-specific mortality

scholarly journals Trends in Recurrent Coronary Heart Disease Following Myocardial Infarction Among US Women and Men Between 2008 and 2017

Circulation ◽  
2020 ◽  
Author(s):  
Sanne A.E. Peters ◽  
Lisandro D. Colantonio ◽  
Yuling Dai ◽  
Hong Zhao ◽  
Vera A. Bittner ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Health Insurance ◽  
Coronary Revascularization ◽  
Heart Failure Hospitalization ◽  
All Cause Mortality ◽  
Adjusted Rate ◽  
Rate Ratios

Background: Rates for recurrent coronary heart disease (CHD) events have declined in the US. However, few studies have assessed whether this decline has been similar among women and men. Methods: Data were used from 770,408 US women and 700,477 US men <65 years of age with commercial health insurance through MarketScan and ≥66 years of age with government health insurance through Medicare who had a myocardial infarction (MI) hospitalization between 2008 and 2017. Women and men were followed for recurrent MI, recurrent CHD events (i.e., recurrent MI or coronary revascularization), heart failure hospitalization, and all-cause mortality (Medicare only) in the 365 days post-MI. Results: From 2008 to 2017, age-standardized recurrent MI rates per 1,000 person-years decreased from 89.2 to 72.3 in women and from 94.2 to 81.3 in men (multivariable-adjusted p-interaction by sex<0.001). Recurrent CHD event rates decreased from 166.3 to 133.3 in women and from 198.1 to 176.8 in men (p-interaction<0.001). Heart failure hospitalization rates decreased from 177.4 to 158.1 in women and from 162.9 to 156.1 in men (p-interaction=0.001). All-cause mortality rates decreased from 403.2 to 389.5 in women and from 436.1 to 417.9 in men (p-interaction=0.82). In 2017, the multivariable-adjusted rate ratios (95%CI), comparing women with men were 0.90 (0.86, 0.93) for recurrent MI, 0.80 (0.78, 0.82) for recurrent CHD events, 0.99 (0.96, 1.01) for heart failure hospitalization, and 0.82 (0.80-0.83) for all-cause mortality. Conclusions: Rates of recurrent MI, recurrent CHD events, heart failure hospitalization, and mortality in the first year after an MI declined considerably between 2008 and 2017 in both men and women, with proportionally greater reductions for women than men. However, rates remain very high and rates of recurrent MI, recurrent CHD events and death continue to be higher among men than women.

Mortality in serious mental illness

2011 ◽  
Vol 26 (S2) ◽  
pp. 2150-2150
Author(s):  
U. Osby
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Bipolar Disorder ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Cerebrovascular Disease ◽  
Causes Of Death ◽  
Mortality Rates ◽  
Rate Ratios ◽  
Bipolar Patients

IntroductionThere is evidence that patients with bipolar disorder have an increased mortality from somatic causes of death, including coronary heart disease and myocardial infarction. However, present mortality ratios and mortality trends over time are not known.AimTo analyze relative mortality and mortality trends for patients with bipolar disorder in relation to the population for cerebrovascular disease, coronary heart disease and myocardial infarction.MethodsAll patients in Sweden with a clinical diagnosis of bipolar disorder from the introduction of ICD-10 (1987–2006) found in the National Swedish Patient Register were followed-up in the Cause of death register. Mortality rate ratios (MRR) for different cardiovascular diseases and different age groups were calculated, as well as numbers of excess deaths, relative to the population. Also, admission rate ratios (ARR) and yearly mortality rates for bipolar patients versus the population were calculated for the same time period.ResultsFrom all causes of death, there were 5,471 deaths for bipolar patients. MRR was 2.58 (95% CI: 2.51–2.65). For cerebrovascular disease MRR was 2.19 (95% CI: 2.01–2.40), and for coronary heart disease MRR was 2.10 (95% CI: 1.98–2.2.24). In the subgroup of acute myocardial infarction MRR was 1.97 (95% CI: 1.81–2.14). In cerebrovascular disease, ARR was increased to 1.47 (95% CI: 1.35–1.59), while in coronary heart disease ARR was 1.06 (95% CI: 0.98–2.24), and in acute myocardial infarction 1.09 (95% CI: 0.0.98–1.22). Yearly mortality rates for these causes of death decreased both among patients and the population, without indication of a decreasing gap.ConclusionsIn patients with bipolar disorder, mortality from cerebrovascular disease and coronary heart disease with its subgroup acute myocardial infarction was doubled during 1987–2006. In contrast, admission rates for coronary heart disease and acute myocardial infarction were not increased. Yearly mortality rates decreased both for the patients and the population, but there were no indications of a decreasing gap.KeywordsBipolar disorder; Register study; Cerebrovascular disease; Coronary heart disease; Acute myocardial infarction; Mortality rate ratios; Admission rate ratios.

scholarly journals Some population mortality indicators of the large region of Volga federal district

2021 ◽  
Vol 21 (1) ◽  
pp. 44-48
Author(s):  
Svetlana V. Ustavshchikova ◽  
Keyword(s):  
Total Population ◽  
Alcohol Exposure ◽  
Federal District ◽  
Mortality Rates ◽  
Physical And Mental Health ◽  
Working Age ◽  
External Causes ◽  
Population Mortality ◽  
Regional Authorities ◽  
Drug And Alcohol

Reducing the mortality rate of the population: general, in childhood and people of working age is a priority goal of the federal and regional authorities. However, an increase in the proportion of the population in older ages in the total population, and an increase in life expectancy leads to an aging of the population as a whole, and, consequently, to an increase (all other things being equal) of the relative mortality rates of the entire population. The potential for mortality reduction is a reduction in mortality due to external causes: from accidents, poisoning, injuries, drug and alcohol exposure. Strengthening the physical and mental health of the population will also contribute to this component decrease.

scholarly journals Defining Rural In Aotearoa New Zealand: A Novel Geographic Classification For Health Purposes

2021 ◽  
Author(s):  
Jesse Whitehead ◽  
Gabrielle Davie ◽  
Brandon de Graaf ◽  
Sue Crengle ◽  
David Fearnley ◽  
...  
Keyword(s):  
New Zealand ◽  
Urban Areas ◽  
Mortality Rates ◽  
Aotearoa New Zealand ◽  
Rural And Urban ◽  
Key Population ◽  
National Mortality ◽  
All Cause Mortality ◽  
Mortality Outcome ◽  
Rate Ratios

Abstract Objectives: To develop a valid rurality classification for health purposes in Aotearoa New Zealand (NZ) that is technically robust and incorporates heuristic understandings of rurality.Setting: Our Geographic Classification for Health (GCH) is developed for all of NZ.Participants: We examine the distribution of the entire NZ population across rurality classifications, and use the National Mortality Collection to examine previously masked rural-urban differences in mortality. Outcome measures: Unadjusted all-cause mortality rates and rural:urban incidence rate ratios (IRRs). Results: The GCH modifies key population and drive time thresholds in the generic rurality classifications, thereby identifying 19% of the NZ population as rural. Rural and urban all-cause mortality rates and associated rural:urban IRRs vary considerably depending on rurality classification. The GCH finds a rural mortality rate 21% higher than for urban areas.Conclusions: The GCH identifies a distinct rural population, and highlights rural-urban inequities that are masked by generic classifications.

Risk Factors for Cardiorespiratory and All Cause Mortality in Men and Women in Urban Scotland: 15 Year Follow Up

1995 ◽  
Vol 40 (4) ◽  
pp. 108-112 ◽  
Author(s):  
G.C.M. Watt ◽  
C.L. Hart ◽  
D.J. Hole ◽  
G.D. Smith ◽  
C.R. Gillis ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Risk Factor ◽  
Heart Disease ◽  
Respiratory Disease ◽  
Mortality Rates ◽  
Respiratory Impairment ◽  
Study Objective ◽  
Men And Women ◽  
All Cause Mortality

Study objective: To describe the relationship between risk factors, risk behaviours, symptoms and mortality from cardiorespiratory diseases in an urban area with high levels of socioeconomic deprivation. A cohort study of 15,411 men and women aged 45–64, comprising 80% of the general population of Paisley and Renfrew, Scotland. Outcomes: Mortality after 15 years from coronary heart disease(ICD 410–4), stroke(ICD 430–8), respiratory disease(ICD 460–519) and all causes. Main results: Mortality rates from all causes were 19% in men aged 45–49, 31% in men aged 50–54, 42% in men aged 55–59 and 57% in men aged 60–64. The rates are considerably higher than those reported in previous UK prospective studies. For women the rates were 12%, 18%, 25% and 38% respectively. In general men and women showed similar relationships between risk factor levels and mortality rates. People in manual occupations had higher mortality rates. Raised levels of systolic and diastolic blood pressure were associated with increased coronary, stroke and all cause mortality rates. Plasma cholesterol had no such association with all cause mortality rates. High and low levels of body mass index were associated with higher mortality rates than intermediate levels. A relationship between short stature and increased mortality rates was observed in men and women. FEV1 expressed as a percentage of the expected value showed the strongest relationship with mortality rates, particularly for respiratory disease, but also for deaths from coronary heart disease, stroke and all causes. Conclusions A similar pattern of relationship between risk factor levels and mortality rates exists in men and women in Renfrew and Paisley. Respiratory impairment as measured by FEV 1% predicted appears to be the most likely explanation of the observed high all cause mortality rates in this population.

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