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

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

Circulation ◽

10.1161/circulationaha.120.047065 ◽

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.

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Mortality in serious mental illness

European Psychiatry ◽

10.1016/s0924-9338(11)73853-6 ◽

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.

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

Scottish Medical Journal ◽

10.1177/003693309504000403 ◽

1995 ◽

Vol 40 (4) ◽

pp. 108-112 ◽

Cited By ~ 28

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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Abstract MP047: Early-Onset Coronary Heart Disease Mortality Remains High Among Rural Blacks

Circulation ◽

10.1161/circ.125.suppl_10.amp047 ◽

2012 ◽

Vol 125 (suppl_10) ◽

Author(s):

Ambar Kulshreshtha ◽

Abhinav Goyal ◽

William McClellan ◽

Emir Veledar ◽

Viola Vaccarino

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Rural Areas ◽

Early Onset ◽

Urban Areas ◽

Coronary Heart Disease Mortality ◽

The United States ◽

Mortality Rates ◽

Substantial Impact ◽

Life Years

Background: Deaths from early-onset coronary heart disease (CHD) translate into a large number of potential life-years lost with substantial impact on families and society. Although overall CHD mortality has declined in the past few decades, the extent to which such decline applies to early CHD mortality and to specific racial groups and urbanization levels has not been examined. We sought to describe the pattern and magnitude of racial and urban-rural variations in early-onset CHD mortality in the United States. Methods: We used data from the National Center for Health Statistics to examine trends in CHD death rates (ICD-10 codes I20-I25) between 1999 and 2007. Early-onset CHD mortality was defined as death due to CHD in men less than 55 yrs or women less than 65 yrs. Rate changes were calculated in the overall population and by race (blacks vs whites) and urbanization (rural vs. urban). Poisson regression was used to model the data. Results: Between 1999 and 2007, there were approximately 400,000 deaths due to early-onset CHD. There was an overall 25% decline in age-adjusted early-onset CHD mortality rates from 79 per 100,000 to 59 per 100,000 but this decline varied by gender, race, and urbanization. Women had a greater decline (27%) compared with men (19%) and blacks had slightly more decline (27%) compared to whites (25%). Urban areas (30%) had twice the decline compared to rural areas (16%). In this period early CHD deaths was higher in blacks than whites and higher in rural than urban areas (Figure). Blacks in rural areas had the highest early-onset CHD mortality, followed by blacks in large metros, while urban whites had the lowest rate. Black-white differences remained similar in urban and rural areas over this time period. Conclusion: The overall decline in early-onset CHD mortality is encouraging, but there are important differences by race and urbanization. Blacks in rural areas have the highest early-onset CHD mortality rates. Early-onset CHD can be used to identify and target groups with high risk in order to reduce disparities.

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The Joy of Statistics

10.1093/oso/9780198833444.001.0001 ◽

2019 ◽

Cited By ~ 1

Author(s):

Steve Selvin

Keyword(s):

Coronary Heart Disease ◽

Short Stories ◽

Heart Disease ◽

Infant Mortality ◽

Statistical Methods ◽

Ethnic Differences ◽

Coronary Heart Disease Risk ◽

Disease Risk ◽

Mortality Rates ◽

Heart Disease Risk

The Joy of Statistics consists of a series of 42 “short stories,” each illustrating how elementary statistical methods are applied to data to produce insight and solutions to the questions data are collected to answer. The text contains brief histories of the evolution of statistical methods and a number of brief biographies of the most famous statisticians of the 20th century. Also throughout are a few statistical jokes, puzzles, and traditional stories. The level of the Joy of Statistics is elementary and explores a variety of statistical applications using graphs and plots, along with detailed and intuitive descriptions and occasionally using a bit of 10th grade mathematics. Examples of a few of the topics are gambling games such as roulette, blackjack, and lotteries as well as more serious subjects such as comparison of black/white infant mortality rates, coronary heart disease risk, and ethnic differences in Hodgkin’s disease. The statistical description of these methods and topics are accompanied by easy to understand explanations labeled “how it works.”

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Lipid profile and prognosis in patients with coronary heart disease: a meta-analysis of prospective cohort studies

BMC Cardiovascular Disorders ◽

10.1186/s12872-020-01835-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiangmei Zhao ◽

Dongying Wang ◽

Lijie Qin

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Cohort Studies ◽

Lipid Profile ◽

Prospective Cohort ◽

Cardiac Death ◽

Meta Analysis ◽

Prospective Cohort Studies ◽

All Cause Mortality ◽

Reduced Risk

Abstract Background This meta-analysis based on prospective cohort studies aimed to evaluate the associations of lipid profiles with the risk of major adverse cardiovascular outcomes in patients with coronary heart disease (CHD). Methods The PubMed, Embase, and Cochrane Library electronic databases were systematically searched for prospective cohort study published through December 2019, and the pooled results were calculated using the random-effects model. Results Twenty-one studies with a total of 76,221 patients with CHD met the inclusion criteria. The per standard deviation (SD) increase in triglyceride was associated with a reduced risk of major adverse cardiovascular events (MACE). Furthermore, the per SD increase in high-density lipoprotein cholesterol (HDL-C) was associated with a reduced risk of cardiac death, whereas patients with lower HDL-C were associated with an increased risk of MACE, all-cause mortality, and cardiac death. Finally, the risk of MACE was significantly increased in patients with CHD with high lipoprotein(a) levels. Conclusions The results of this study suggested that lipid profile variables could predict major cardiovascular outcomes and all-cause mortality in patients with CHD.

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Mortality and associated risk factors in diabetics

Acta Endocrinologica ◽

10.1530/acta.0.110s021 ◽

1985 ◽

Vol 110 (4_Suppl) ◽

pp. S21-S26 ◽

Cited By ~ 33

Author(s):

R. J. Jarrett ◽

M. J. Shipley

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Coronary Heart Disease ◽

Heart Disease ◽

Mortality Risk ◽

Mortality Rates ◽

Potential Risk Factors ◽

Diabetic Population ◽

Associated Risk Factors ◽

The Relationship

Summary. In 168 male diabetics aged 40-64 years participating in the Whitehall Study, ten-year age adjusted mortality rates were significantly higher than in non-diabetics for all causes, coronary heart disease, all cardiovascular disease and, in addition, causes other than cardiovascular. Mortality rates were not significantly related to known duration of the diabetes. The predictive effects of several major mortality risk factors were similar in diabetics and non-diabetics. Excess mortality rates in the diabetics could not be attributed to differences in levels of blood pressure or any other of the major risk factors measured. Key words: diabetics; mortality rates; risk factors; coronary heart disease. There are many studies documenting higher mortality rates - particularly from cardiovascular disease -in diabetics compared with age and sex matched diabetics from the same population (see Jarrett et al. (1982) for review). However, there is sparse information relating potential risk factors to subsequent mortality within a diabetic population, information which might help to explain the increased mortality risk and also suggest preventive therapeutic approaches. In the Whitehall Study, a number of established diabetics participated in the screening programme and data on mortality rates up to ten years after screening are available. We present here a comparison of diabetics and non-diabetics in terms of relative mortality rates and the influence of conventional risk factors as well as an analysis of the relationship between duration of diabetes and mortality risk.

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