A population-based model of respiratory cancer incidence, progression, diagnosis, treatment, and mortality

Mantón, Kenneth G.; Stallard, Eric

doi:10.1016/0010-4809(82)90003-9

Cited by 23 publications

(14 citation statements)

References 8 publications

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“…Since [e.g., Manton and Stallard, 1982] average lags (/ c ) of 10 to 40 years are reported between the triggering event, and lung cancer death, we tested Z c s in (20) of different lengths bounded above by the age of the cohort minus 15 years as a guarantee time. The l c , 6^ (nonsmokers) and #2 (smokers), "relative risk," "hit" parameter a, shape parameter, 7, and the CV, are in Table 1 for n = 1.0 for each cohort.…”

Section: Resultsmentioning

confidence: 99%

“…The parameter / was added to the vector £, i.e., we used MmCy + U6 m ,a,l) = Om(y + t -Z)"" 1 in (11). / is the time in years between tumor initiation and death from the tumor (Manton and Stallard, 1982).…”

Section: Example: Methods and Datamentioning

confidence: 99%

“…These were developed to describe drug metabolism in unobserved physiological "compartments" and have been used in other biological problems (Matis and Wehrly, 1979), e.g., to describe the population "kinetics" of morbidity (e.g., Manton and Stallard, 1984). To construct a model, biological information is required on the time dependence of health transitions, e.g., the age dependence of disease incidence, the rate of tumor growth, median survival time in a disease stage (Manton and Stallard, 1982;. This may be derived from clinical, or laboratory data, from which biological constants of processes, and not the mix of processes over individuals in the population are derived.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Methods for combining ancillary data in stochastic compartment models of cancer mortality: Generalization of heterogeneity models∗

Mantón

Lowrimore

Yashin

1993

Mathematical Population Studies

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"We present a mortality model where nationally representative survey data on risk factor distributions are combined with data on cohort mortality rates to increase information, i.e., a fixed marginal risk factor distribution is combined with a cohort model representing unobserved individual risk heterogeneity. The model is applied to lung cancer mortality in nine U.S. white male cohorts aged 30 to 70 in 1950 and followed 38 years. Estimates of the cohort specific proportions of smokers were made from the National Health Interview Survey. Comparisons are made for models with different patterns of changes with age of individual heterogeneity." (SUMMARY IN FRE)

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Section: Resultsmentioning

confidence: 99%

Section: Example: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methods for combining ancillary data in stochastic compartment models of cancer mortality: Generalization of heterogeneity models∗

Mantón

Lowrimore

Yashin

1993

Mathematical Population Studies

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“…(6) where numerical procedures described in Ref. (10) are employed in evaluating this function at the midpoint of each age interval. The triple subscript on A1,2,3 indicates that this function combines the three transitions from the well state to respiratory cancer death indicated in Fig.…”

Section: -mentioning

confidence: 99%

The Economic Impact of Health Policy Interventions

1983

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The rapid aging of the U.S. population, increases in the absolute prevalence of chronic diseases, and the associated rise in the proportion of the GNP expended on medical care all indicate the need for methods to accurately forecast future health care expenditures for specific chronic diseases. Additionally, if these methods are biomedically realistic, they can be used to evaluate the economic implications of specific prevention strategies designed to reduce chronic disease incidence, prevalence, and mortality. Projection strategies that are not biomedically realistic, such as models that assume that risks for demographic subgroups do not change over time (e.g., "static component" models), though possibly accurate over the short run, are not suitable for assessing the long term effects of specific proposed health policy interventions which are designed to alter risks.In this paper we present a strategy for forecasting health care costs which is based on a model that represents the natural history of a chronic disease in terms of a preclinical state, a clinical state, case fatality rates, cures, and the implications of exogenous medical factors. Using this model we project that the treatment costs associated with respiratory cancer in the white male population of the U.S. may undergo a two-thirds increase in real dollars over the period 1977 to 2000. About one-half of this increase is due to a demographic shift to an older population structure, with the remainder due to higher respiratory cancer incidence rates in younger cohorts. Alteration of certain parameters of the model to simulate various interventions suggests that about three-quarters of the cost of this disease could be eliminated, though realization of any significant part of this savings would require a lengthy phase-in period.KEY WORDS: Health status projections; actuarial cost calculations; respiratory cancer risks; multistage models of carcinogenesis.

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“…This can be done because the heterogeneity model provides an appropriate conceptual framework for considering the effects of dependence among several causes of death (11,20,21 The parameters of the gamma/Weibull function in Eq. (4) can be estimated by using a Poisson likelihood function based on the counts of the number of deaths and the person years at risk (10,23). For the Poisson function, the maximum likelihood estimates of X(a,c) for the fully saturated model (i.e., same number of parameters as observations) are precisely the observed death rates, computed as in Eq.…”

Section: Cohort-specific Analysesmentioning

confidence: 99%

U.S. Cancer Mortality 1950-1978: A Strategy for Analyzing Spatial and Temporal Patterns

Mantón¹,

Stallard²,

Creason³

et al. 1985

Environmental Health Perspectives

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There are a number of technical and statistical problems in monitoring the temporal and spatial variation of local area death rates in the United States for evidence of systematically elevated risks. An analytic strategy is proposed to reduce one of the major statistical concerns, i.e., that of identifying areas with truly elevated mortality risks from a large number of local area comparisons. This analytic strategy involves two stages. The first is a procedure for examining the entire distribution of local area death rates instead of simply selecting high risk "outliers." The second is the development of an analytic procedure to relate the temporal changes in the cross-sectional distribution of local area death rates to models of the disease process operating within the populations in those areas. The procedures are applied to data on cancer mortality for the 3050 counties (or county equivalents) of the United States over the period 1950 to 1978. A number of striking mortality patterns, both within the entire United States and within various regions and states, are identified. For example, perhaps the most persistent finding was that the risk increases in the death rates for respiratory cancer mortality were due to a "catching up" of nonmetropolitan county mortality rates with metropolitan area mortality rates.

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A population-based model of respiratory cancer incidence, progression, diagnosis, treatment, and mortality

Cited by 23 publications

References 8 publications

Methods for combining ancillary data in stochastic compartment models of cancer mortality: Generalization of heterogeneity models∗

Methods for combining ancillary data in stochastic compartment models of cancer mortality: Generalization of heterogeneity models∗

The Economic Impact of Health Policy Interventions

U.S. Cancer Mortality 1950-1978: A Strategy for Analyzing Spatial and Temporal Patterns

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