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Competing Risks Analysis Using Markov Chains: Impact of Cerebrovascular and Ischaemic Heart Disease in Cancer Mortality
Abstract
A decrease in cerebrovascular disease (CVD) and ischaemic heart disease (IHD) mortality can produce an increase in mortality from other causes, even cancer. This problem is called the competing risks problem.
A Markov chain is used to analyse the interrelation between CVD, IHD and cancer mortalities in Spanish women in 1981 and 1994. We compare the results using two models: discarding CVD and IHD mortality (the elimination model) and substituting CVD and IHD 1981 mortality rates in 1994 figures (the constant model).
Removing mortality from CVD and IHD increases cancer mortality rates in women aged > or = 70, and the probability of death from cancer rises from 10.7% to 13.3%. In the second model, the use of CVD and IHD 1981 mortality rates in 1994 data yields slightly lower mortality rates and so the impact of CVD and IHD mortality changes in the period 1981 to 1994 is negligible except in elderly women.
Although IHD and CVD mortality have decreased in all age groups of Spanish women from 1981 to 1994, this has not had a great impact on cancer mortality.
... The influence of cerebrovascular and cardiovascular disease mortality trends on cancer mortality trends has been analysed in various studies. Llorca et al analysed the interrelation between cerebrovascular disease, ischemic heart disease and cancer mortalities in Spanish women in 1981 and 1994 using both models and showed that although cerebrovascular and ischemic heart disease mortality have decreased in all age groups during the investigation period, this had not a significant impact on cancer mortality [47] . However, investigation of competing risks usually assumes independence between the different causes of death. ...
... A prominent example of a common risk factor between cardiovascular, cerebrovascular disease and various cancer entities is tobacco smoking. The error that is produced by the assumption of independence between those diseases is therefore influenced on the smoking prevalence of the investigated population and on the level of mortality from tobacco-related cancers [47] . The results of our systematic review cannot provide safe conlusions on competing risks, since they compare long term survival of different populations and do not provide information on risk factors that are important for analysis. ...
Cancer, heart failure and stroke are among the most common causes of death worldwide. Investigation of the prognostic impact of each disease is important, especially for a better understanding of competing risks. Aim of this study is to provide an overview of long term survival of cancer, heart failure and stroke patients based on the results of large population- and hospital-based studies.
Records for our study were identified by searches of Medline via Pubmed. We focused on observed and relative age- and sex-adjusted 5-year survival rates for cancer in general and for the four most common malignancies in developed countries, i.e. lung, breast, prostate and colorectal cancer, as well as for heart failure and stroke.
Twenty studies were identified and included for analysis. Five-year observed survival was about 43% for all cancer entities, 40-68% for stroke and 26-52% for heart failure. Five-year age and sex adjusted relative survival was 50-57% for all cancer entities, about 50% for stroke and about 62% for heart failure. In regard to the four most common malignancies in developed countries 5-year relative survival was 12-18% for lung cancer, 73-89% for breast cancer, 50-99% for prostate cancer and about 43-63% for colorectal cancer. Trend analysis revealed a survival improvement over the last decades.
The results indicate that long term survival and prognosis of cancer is not necessarily worse than that of heart failure and stroke. However, a comparison of the prognostic impact of the different diseases is limited, corroborating the necessity for further systematic investigation of competing risks.
... The transition probabilities between states may be calibrated from cohort data (Duffy et al., 1995) for simulations of likely disease progression. The resultant toolkit has applications in both medicine (Llorca and Delgado-Rodríguez, 2001) and health economics (Le Lay et al., 2007). ...
This paper develops and analyzes a Markov chain model for the treatment of cancer. Cancer therapy is modeled as the patient’s Markov Decision Problem, with the objective of maximizing the patient’s discounted expected quality of life years. Patients make decisions on the duration of therapy based on the progression of the disease as well as their own preferences. We obtain a powerful analytic decision tool through which patients may select their preferred treatment strategy. We illustrate the tradeoffs patients in a numerical example and calculate the value lost to a cohort in suboptimal strategies. In a second model patients may make choices to include drug holidays. By delaying therapy, the patient temporarily forgoes the gains of therapy in order to delay its side effects. We obtain an analytic tool that allows numerical approximations of the optimal times of delay.
... The transition probabilities of such models may be calibrated from cohort data (Duffy et al., 1995) for simulations of likely disease progression. The resultant toolkit has applications in both medicine (Llorca et al., 2001) and health economics (Le Lay et al., 2007). Crucially, in Markovian models, the transition probabilities are assumed to depend only on the current state of the patient, not on previous disease history. ...
This paper develops and analyzes a Markov chain model for the treatment of cancer. Cancer therapy is modeled as the patient's Markov Decision Problem, with the objective of maximizing the patient's discounted expected quality of life years. Patients choose the number of treatment rounds they wish to administer based on the progression of the disease as well as their own preferences. We obtain a powerful analytic decision tool by which patients may select their preferred treatment strategy. In a second model patients may make choices on the timing of treatment rounds as well. By delaying a round of therapy the patient forgoes the gains of therapy for a time in order to delay its side effects. We obtain an analytic tool that allows numerical approximations of the optimal times of delay.
... In addition, Markov Chains were used in simulation [Bré13], medicine [LD01], economics [CR01] and many other fields that handle random processes, due to their power of explicitly showing the different probabilities and paths of reaching or avoiding an objective (state) starting from a certain point. However, this modeling formalism is confronted with a big limitation: the size of a Markov model can quickly increase in order to model large systems (or random processes) making it hard to compute and handle. ...
The main objective of this thesisis to combine the advantages of probabilisticgraphical model learning and formal verifica-tion in order to build a novel strategy for secu-rity assessments. The second objective is toassess the security of a given system by veri-fying whether it satisfies given properties and,if not, how far is it from satisfying them. Weare interested in performing formal verificationof this system based on event sequences col-lected from its execution. Consequently, wepropose a model-based approach where a Re-cursive Timescale Graphical Event Model (RT-GEM), learned from the event streams, is con-sidered to be representative of the underlyingsystem. This model is then used to check a se-curity property. If the property is not verified,we propose a search methodology to find an-other close model that satisfies it. We discussand justify the different techniques we use inour approach and we adapt a distance mea-sure between Graphical Event Models. Thedistance measure between the learned "fittest"model and the found proximal secure modelgives an insight on how far our real system isfrom verifying the given property. For the sakeof completeness, we propose series of exper-iments on synthetic data allowing to provideexperimental evidence that we can attain thedesired goals.
... Using such a model, Rothenberg (1994) established that the contribution of the CHD mortality decline to the increase in cancer mortality has been small and does not account for the increasing age-specific risk of cancer among older persons. Llorca and Delgado-Rodriguez (2001) used a Markov chain model to analyze interrelations between CVD, CHD, and cancer in Spanish females. They found that declines in CVD and CHD mortality did not have an impact on cancer mortality. ...
Demographic calculations evaluating the role of chronic diseases in life expectancy use the assumption that diseases are independent. Disease independence was a plausible hypothesis in the era of infectious diseases. However, the health problems of modern populations are closely connected with diseases of the elderly i.e., with chronic non-communicable diseases that often have common risk factors. The existence of such common genetic and non-genetic risk factors makes chronic diseases mutually dependent. In this chapter, we provide evidence of trade-offs between cancer and other diseases as well as between cancer and aging changes. The Multiple Cause of Death data are used to evaluate correlations among mortality rules from cancer and other major health disorders, including heart disease, stroke, diabetes, Alzheimer’s and Parkinson’s diseases, and asthma. Significant negative correlations between cancer and some of the selected diseases are detected. These correlations show regular patterns of change over time. The chapter describes possible mechanisms of disease dependence including pleiotropic effects of genetic factors and discusses appropriate methods of statistical analysis of disease dependence.
... Lin 13 realiza un abordaje similar y aporta un test adecuado para comparar 2 curvas de incidencia acumulada (en sustitución del test de rangos logarítmicos que, como ocurre con el método de Kaplan-Meier, es inadecuado en presencia de riesgos competitivos). Otra alternativa es el empleo de cadenas de Markov en combinación con las fórmulas para riesgos competitivos de Chiang 14 ; esta opción es muy flexible y permite analizar cualquiera de los modelos de los apartados 3-5, si es posible admitir la hipótesis markoviana de que el futuro sólo depende del estado actual y no de la forma de llegar a él 15 . ...
Objective
To show the impact of competing risks of death on survival analysis.Method
We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model.ResultsThe Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks.Conclusions
Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.
... Yashin et al. (2009) assumed a multivariate log-normal frailty model in their approach of a dependent competing risks model capturing negative correlations between causes of death. Llorca and Delgado-Rodríguez (2001) developed a Markov chain model to study the association between cardiovascular disease, coronary heart disease and cancer in Spain. Honoré and Lleras-Muney (2006) developed a solution for the competing risk model in a semiparametric accelerated failure time model with grouped durations. ...
For over forty years, demographers have worked intensely to develop methods that assess a gain in life expectancy from a reduction in mortality, either hypothetical or observed. This considerable body of research was motivated by assessing the gains in life expectancy when mortality declined in a particular manner and determining the contribution of a cause of death in observed changes in life expectancy over time. As yet, there has been no framework unifying this important demographic work. In this paper, we provide a unifying framework for assessing the change in life expectancy given a change in age- and cause-specific mortality. We consider both conceptualizations of mortality change-counterfactual assessment of a hypothetical change and a retrospective assessment of an observed change. We apply our methodology to violent deaths, the leading cause of death among young adults, and show that realistic targeted reductions could have important impacts on life expectancy.
... Llorca and Delgado-Rodríguez present an approach based on Markov chains to evaluate this question of competing risks. 1 The statistical theory behind the use of Markov chains in the analyses of competing risks is well described 2 (textbox). The merit of their paper is therefore to illustrate that the different approaches previously used on this subject in the epidemiological literature can be unified. ...
... Using such model, Rothenberg (1994) established that contribution of the CHD mortality decline to the increase in cancer mortality has been small and does not account for the increasing age-specific risk of cancer among older persons. Llorca and Delgado-Rodriguez (2001) used the Markov chain model to analyze interrelation between CVD, CHD, and cancer in Spanish females. They found that declines in CVD and CHD mortality did not have an impact on cancer mortality. ...
The potential gain in life expectancy which could result from the complete elimination of mortality from cancer in the U.S. would not exceed 3 years if one were to consider cancer independently of other causes of death. In this paper, we review evidence of trade-offs between cancer and aging as well as between cancer and other diseases, which, if taken into account, may substantially increase estimates of gain in life expectancy resulting from cancer eradication. We also used the Multiple Causes of Death (MCD) data to evaluate correlations among mortalities from cancer and other major disorders including heart disease, stroke, diabetes, Alzheimer's, Parkinson's diseases, and asthma. Our analyses revealed significant negative correlations between cancer and other diseases suggesting stronger population effects of cancer eradication. Possible mechanisms of the observed dependencies and emerging perspectives of using dependent competing risks models for evaluating the effects of reduction of mortality from cancer on life expectancy are discussed.
... Chiang (1991),Rothenberg (1994), andLlorca and Delgado-Rodriguez (2001) investigated the effects of CVD trends on trends in cancer mortality, but they assumed that risks are independent(Wohlfart and Andersen (2001)).4 Other papers have used a similar approach to construct bounds on objects of interest in semior nonparametric models. ...
In 1971, President Nixon declared war on cancer. Thirty years later, many declared this war a failure: the age-adjusted mortality rate from cancer in 2000 was essentially the same as in the early 1970s. Meanwhile the age-adjusted mortality rate from cardiovascular disease fell dramatically. Since the causes that underlie cancer and cardiovascular disease are likely dependent, the decline in mortality rates from cardiovascular disease may partially explain the lack of progress in cancer mortality. Because competing risks models (used to model mortality from multiple causes) are fundamentally unidentified, it is difficult to estimate cancer trends. We derive bounds for aspects of the underlying distributions without assuming that the underlying risks are independent. We then estimate changes in cancer and cardiovascular mortality since 1970. The bounds for the change in duration until death for either cause are fairly tight and suggest much larger improvements in cancer than previously estimated. Copyright The Econometric Society 2006.
A phase‐type distribution is the distribution of time to absorption for an absorbing continuous‐time finite state Markov chain. The paper first reviews the extension of the phase‐type setting to modeling of competing risks by introducing multiple absorbing states. The main study of the paper is the further extension to introducing instantaneous transitions at certain stages of the original models. The motivation is from applications to repair and maintenance, bringing failed systems into working ones by instantaneous repair actions. Two slightly different approaches are studied. The first one is based on restarting the original Markov chain upon absorption, leading to the consideration of a Markov renewal process. The second approach involves periodically inspected systems, where maintenance actions are modeled by instantaneous transitions made at regular inspection times. For both approaches are suggested measures of reliability and maintenance based on long run properties.
Collaborative networks are characterised by the establishment of relations in more or less hierarchical power structures. The hierarchy of the network is defined by the partners' power degree. Hierarchical structures and associated barriers limit the decision making and discourage collaboration within partners. This paper focuses on proposing a method to allow researchers to identify the power degree of each network partner, through Markov Chains. Knowing the power distribution, helps researchers to diagnose the power balance, reconsider the status in the network and have a better view of power interaction and collaboration. Therefore, the power distribution analysis is a key issue to understand the partners' behaviour and achieve sustainable networks. © IFIP International Federation for Information Processing 2013.
Background: To assess public health relevance of targets on tobacco smoking, information is needed on the decline in future mortality following smoking cessation. WHO's Health for All (HFA) and other targets on tobacco smoking in the European Union (EU) were therefore simulated. Methods: A computer simulation model, Markov multi-state type, was developed using published age- and sex-specific information on population sizes, smoking prevalences, total and cause-specific mortality rates and corresponding relative risks for ex- and current smokers. The probabilities to start and quit smoking (transition rates) were estimated. Targets on smoking cessation included WHO's HFA target (country-specific smoking prevalence is reduced to 20% by 2015), and a theoretical maximum target (all smokers quit instantly). Simulation of these targets was based on changes in transition rates for smoking behaviour. For each target, the cumulated number of all-cause and cause-specific deaths between 1994 and 2015 was estimated for each EU member state. Then, the absolute and relative reduction in the number of deaths compared to a reference scenario, in which transition rates for smoking behaviour remained constant, were estimated for the EU as a whole. Results: WHO's HFA target was expected to give a total mortality reduction by 2015 of 2.5% (around 1.1 million deaths) among men and 0.8% (almost 350,000 deaths) among women in the EU. Overall, the expected mortality declines in the HFA target were about 40–50% (men) and 30% (women) of the expected declines in the maximum target. The largest impact of the HFA target would be reached for lung cancer mortality. Conclusions: These results emphasize the need for policymakers in each EU member state to put strong effort into encouraging smokers to quit smoking.
Objective: To determine the residual lifetime and 10-year absolute risks of osteoporotic fractures in Chinese men and women.
Methods: A validated state-transition microsimulation model was used. Microsimulation and probabilistic sensitivity analyses were performed to address the uncertainties in the model. All parameters including fracture incidence rates and mortality rates were retrieved from published literatures. Simulated subjects were run through the model until they died to estimate the residual lifetime fracture risks. A 10-year time horizon was used to determine the 10-year fracture risks. We estimated the risk of only the first osteoporotic fracture during the simulation time horizon.
Results: The residual lifetime and 10-year risks of having the first osteoporotic (hip, clinical vertebral or wrist) fracture for Chinese women aged 50 years were 40.9% (95% CI: 38.3-44.0%) and 8.2% (95% CI: 6.8-9.3%) respectively. For men, the residual lifetime and 10-year fracture risks were 8.7% (95% CI: 7.5-9.8%) and 1.2% (95% CI: 0.8-1.7%) respectively. The residual lifetime fracture risks declined with age, whilst the 10-year fracture risks increased with age until the short-term mortality risks outstripped the fracture risks. Residual lifetime and 10-year clinical vertebral fracture risks were higher than those of hip and wrist fractures in both sexes.
Conclusions: More than one third of the Chinese women and approximately one tenth of the Chinese men aged 50 years are expected to sustain a major osteoporotic fracture in their remaining lifetimes. Due to increased fracture risks and rapidly ageing population, osteoporosis will present a great challenge to the Chinese healthcare system.
Limitations: While national data was used wherever possible, regional Chinese hip and clinical vertebral fracture incidence rates were used, wrist fracture rates were taken from a Norwegian study and calibrated to the Chinese population. Other fracture sites like tibia, humerus, ribs and pelvis were not included in the analysis, thus these risks are likely to be underestimates. Fracture risk factors other than age and sex were not included in the model. Point estimates were used for fracture incidence rates, osteoporosis prevalence and mortality rates for the general population.
Read More: http://informahealthcare.com/doi/abs/10.1185/03007995.2015.1037729
During Saskatchewan's healthcare reform of the 1990s, the number of acute hospital beds in Saskatoon District Health was cut in half. The emergency room and outpatient facilities were not able to accommodate an increasing number of patients needing urgent assessments and medical procedures. In this article, we describe the development, implementation and utilization of a day medicine program. This description may be useful to others planning healthcare delivery to medical patients, especially in a setting of resource constraint.
To assess public health relevance of targets on tobacco smoking, information is needed on the decline in future mortality following smoking cessation. WHO's Health for All (HFA) and other targets on tobacco smoking in the European Union (EU) were therefore simulated.
A computer simulation model, Markov multi-state type, was developed using published age- and sex-specific information on population sizes, smoking prevalences, total and cause-specific mortality rates and corresponding relative risks for ex- and current smokers. The probabilities to start and quit smoking (transition rates) were estimated. Targets on smoking cessation included WHO's HFA target (country-specific smoking prevalence is reduced to 20% by 2015), and a theoretical maximum target (all smokers quit instantly). Simulation of these targets was based on changes in transition rates for smoking behaviour. For each target, the cumulated number of all-cause and cause-specific deaths between 1994 and 2015 was estimated for each EU member state. Then, the absolute and relative reduction in the number of deaths compared to a reference scenario, in which transition rates for smoking behaviour remained constant, were estimated for the EU as a whole.
WHO's HFA target was expected to give a total mortality reduction by 2015 of 2.5% (around 1.1 million deaths) among men and 0.8% (almost 350,000 deaths) among women in the EU. Overall, the expected mortality declines in the HFA target were about 40-50% (men) and 30% (women) of the expected declines in the maximum target. The largest impact of the HFA target would be reached for lung cancer mortality.
These results emphasize the need for policymakers in each EU member state to put strong effort into encouraging smokers to quit smoking.
Objective:
To show the impact of competing risks of death on survival analysis.
Method:
We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model.
Results:
The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks.
Conclusions:
Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.
Benefits and risks of a combined hormone replacement therapy (HRT) based on randomized clinical trial emerged on various disease endpoints in 2002. The Women's Health Initiative (WHI) provides an important health answer for healthy postmenopausal women, such as do not use combined HRT to prevent chronic disease, because of the elevated risk of coronary artery disease (CHD), stroke and venous thromboembolism. In March 2004, the NIH stopped the drugs in the estrogen-alone trial after finding an increase risk of stroke and no effect, neither an increase or a decrease, on risk of CHD after an average of 7 years in the trial. On the other hand, raloxifene, which does not seem to significantly increase the risk of cardiovascular events and could retain skeletal benefits without stimulating endometrial and breast tissue, requires decision-makers since no current data on these disease clinical endpoints have been published.
To construct a multi-disease model based on patient-specific risk factor profiles, and to validate the multi-disease model with several tools of internal and external validities.
A Markov state model was developed. The risks of these various diseases (including coronary artery disease, stroke, hip fracture and breast cancer) are derived from published hazards proportional models which take into account significant risk factors. Canadian-specific rates and data sources for these transition probabilities are derived from published studies and Canadian Health Statistics. The validation of our model were based on several tools of internal and external validities, such as Canadian life expectancy, population-based incidence rate of diseases, clinical trials and other published life expectancy models.
First, presumably, small changes in the lifetime probability of dying support the hypothesis that the disease states operate in a largely independent fashion. For instance, the difference in the probability of dying from a particular disease by the complete elimination of a selected disease, such as CHD, stroke or breast cancer, ranged from 0.2 to 2.2% of difference in the lifetime probability of dying of these diseases. Second, we demonstrated that the model adequately predicted the Canadian population lifetable and disease-incidence rates from population-based data among women from 45 to 75 years old. The predictions of the model were cross-checked from non-source data, such as predicted outcomes versus observed outcomes from results of clinical trials. Predicted relative risks of CHD event, breast cancer and hip fracture fell in the reported 95% confidence interval of clinical trials. Finally, predicted treatment benefits are comparable with those of published life expectancy models.
The results of the study demonstrated that this multi-disease model, including coronary artery disease, stroke, hip fracture and breast cancer, is a valid model to predict the impact on life expectancy or number of events prevented for preventive pharmacological interventions.
This paper forecasts the consequences of scientific progress in cancer for total Medicare spending between 2005 and 2030. Because technological advance is uncertain, widely varying scenarios are modeled. A baseline scenario assumes that year 2000 technology stays frozen. A second scenario incorporates recent cancer treatment advances and their attendant discomfort. Optimistic scenarios analyzed include the discovery of an inexpensive cure, a vaccine that prevents cancer, and vastly improved screening techniques. Applying the Future Elderly Model, we find that no scenario holds major promise for guaranteeing the future financial health of Medicare.
Introduction and objectives. In Spain ischemic heart disease mortality was increasing prior to 1975 and has decreased since 1975. This trend is common to both genders. The goal of this paper is to separate the genetic, environmental and competitive risk factors influencing this evolution. Methods. The Gompertz function was adjusted cross-sectionally to age-specific mortality due to ischemic heart disease for each year from 1951 to 1992. The Gompertzian longitudinal analysis was applied to the coefficients obtained to estimate the effect due to environmental and competitive factors. Results. Ischemic heart disease in Spain is a Gompertzian disease with an intersection point at 67 years for men and 40 years for women. Environmental factors were increasing before 1975 and have decreased since then. However, the competitive factors decreased in men since 1980 and in women since 1951 on. Conclusion. The evolution of risk factors (smoking, hypercholesterolemia and hypertension) is responsible for the major proportion of ischemic disease mortality changes. Treatment of instaured ischemic disease has a low influence.
This paper is in response to an article by Kimball [1969] on evaluation of the partial crude probabilities in the theory of competing risks. Contrary to Kimball's conclusions, it is shown that the general model of competing risks under the proportionality assumption satisfies the criteria of internal consistency and reasonableness in describing survival and death processes, and that the conditional probability suggested as a substitute for the partial crude probability violates these criteria.
Introduction and objectives
In Spain ischemic heart disease mortality was increasing prior to 1975 and has decreased since 1975. This trend is common to both genders. The goal of this paper is to separate the genetic, environmental and competitive risk factors influencing this evolution.
Methods
The Gompertz function was adjusted cross-sectionally to age-specific mortality due to ischemic heart disease for each year from 1951 to 1992. The Gompertzian longitudinal analysis was applied to the coefficients obtained to estimate the effect due to environmental and competitive factors.
Results
Ischemic heart disease in Spain is a Gompertzian disease with an intersection point at 67 years for men and 40 years for women. Environmental factors were increasing before 1975 and have decreased since then. However, the competitive factors decreased in men since 1980 and in women since 1951 on.
Conclusion
The evolution of risk factors (smoking, hypercholesterolemia and hypertension) is responsible for the major proportion of ischemic disease mortality changes. Treatment of instaured ischemic disease has a low influence.
Trends of ischaemic heart disease (IHD) mortality and of many of their determinants in Spain from 1940–1988 resemble those of other Mediterranean countries and some Western countries with a lag of 10 years. Mortality from IHD Increased from 1955 to 1975 and then declined. This trend affected both sexes and almost all age groups. Tobacco smoking increased from 3.2 g per capita per day in 1940 to 8 g per capita per day in the mid-1970s and then levelled off. The prevalence of smoking has decreased in all ages among men but has Increased among women over the last 15 years. Since 1940 there has been a steady increase in the intake of meat and dairy products and a moderate rise of eggs, fish and sunflower oil. Consumption of olive oil has remained relatively constant. These trends have been translated into an increase of total lipid intake and of saturated, monounsaturated and polyunsaturated fatty acids and cholesterol over the period 1940–1988. The number of physicians, primary health care centres, cardiac function diagnostic facilities, heart operations and the consumption of cardiovascular drugs have also Increased since the mid- 1970s. The authors conclude that the reduction in smoking among men and the improvement of the health care system may have contributed to the decline in IHD mortality rates in Spain since 1975.
The immediate effect of discovering a way to cure cancer would be a reduction in the number of deaths in the United States by the number of people now dying from that cause. Within a short time, however, deaths from other causes would increase, and the net long-term effect would be relatively small. A parameter is derived that measures how much the expectation of life is increased by a marginal reduction in any cause of death. That parameter is additive in the several causes and has other advantages, though it does not avoid the assumption of independence.
In this paper we present a brief review of the concept of competing risks and the statistical methods of mortality analysis including estimation of three types of probability of dying with respect to a particular cause of death. We will describe formulas of estimates for cohort studies medical follow-up studies and analyses of mortality data for a current population. To illustrate this method of analysis we will use the major cardiovascular (CV) diseases and malignant neoplasms mortality data of the United States white male and female population in 1986. (EXCERPT)
This paper reviews the recent international data on trends in coronary heart disease mortality and morbidity and risk factor levels and assesses the possible explanations for the changes in mortality rates. The implications of the trends and the associated investigations for the prevention and control of coronary heart disease and other noncommunicable diseases are discussed. (EXCERPT)
Mortality from coronary heart disease (CHD) increased among Swedish men between 1968 and 1981, but after that, began to decline.
CHD mortality in women decreased slightly, mostly among older women. From 1980, the incidence of non-fatal myocardial infarction
(MI) started to decrease among men. Among middle-aged women, however, there was a significantly increased incidence.
Mortality during the two years following hospital discharge decreased both in men and women between 1968 and 1985 in Gothenburg.
Between one-sixth and one-fifth of major CHD events occur among patients with previous MI or angina pectoris.
Serum cholesterol and smoking habits increased among middle-aged men from 1963 to 1973, but decreased thereafter. Blood pressure
decreased, and the percentage of people on treatment increased. Blood pressure and serum cholesterol decreased among middle-aged
women, but smoking and triglycerides increased. These different trends might explain an increasing CHD incidence among younger
women but decreasing incidence and mortality among older women.
The decline in ischemic heart disease (IHD) mortality in the United States in recent years is thought to have contributed to increases in cancer mortality. To estimate the interrelation between these competing causes of death between 1970 and 1988, I constructed a hypothetical population schedule by assuming that age-specific IHD mortality risks had not declined. The difference between the actual population and the hypothetical population represents persons who did not die from IHD and were thus available to die from cancer. Using observed age-specific cancer risks over the entire interval, 153,207 of the 7,649,058 cancer deaths (2.0%) in persons age 20-85 years occurred in IHD survivors; in 1988, 24,053 of the 482,490 cancer deaths (5.0%) occurred in IHD survivors. Among 55 to 85 year olds in 1988, IHD survivors accounted for 5.5% of the cancer deaths. Alternative assumptions about the susceptibility of IHD survivors to cancer have little impact on the contribution of IHD survivors to cancer deaths. Results from a separate analysis demonstrated that the proportional contribution of true cancer risk to the increase in cancer cases tripled in the interval 1970-1988 compared with the interval 1930-1970. These observations indicate that the contribution of the IHD mortality decline to the increase in cancer mortality has been small and does not account for the increasing age-specific risks for cancer among older persons.
Coronary heart disease in Sweden: mortality, incidence and risk factors over 20 years in Gothenburg
- L Wilhelmsen
- S Hohansson
- G Ulvenstam