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Changing mortality for motor neuron disease in France (1968–2007): an age-period-cohort analysis

  • NEURO-EPIDEMIOLOGY
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Abstract

The incidence and mortality of motor neuron disease (MND) increase with age and appear to have increased with time. The examination of period and cohort effects using age-period-cohort (APC) models can help characterize temporal trends. Our objective was to describe mortality from MND in France (1968–2007), and to examine the role of age, period of death, and birth-cohort on changes in mortality. The number of people who died from MND and population statistics (1968–2007) were extracted from French national records. Annual standardized (age/sex) mortality ratios (SMRs) were computed. Using Poisson regression, APC models examined the relationship between mortality rates and age, period of death, and birth-cohort in subjects aged 40–89 years. Deviance/degrees-of-freedom ratios evaluated model fit; ratios close to one indicated adequate fit. Between 1968 and 2007, 38,863 individuals died from MND (mortality rate = 1.74/100,000); 37,624 were aged 40–89 years. SMRs increased from 54 (95% CI = 49–59) in 1968 to 126 (120–132) in 2007. Male-to-female ratios declined from 1.80 in 1968 to 1.45 in 2007. Changing mortality rates were best explained by cohort effects (deviance/degrees-of-freedom = 1.09). The relative risk of dying from MND increased markedly for persons born between 1880 and 1920, and more slowly after 1920. In conclusion, mortality rates for MND increased between 1968 and 2007, and more rapidly in women than men. This increase was better explained by the birth-cohort of individuals than by period effects. Changing environmental exposures may be a possible explanation and these findings warrant the continued search for environmental risk factors for MND.

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Abbreviations

AIC:

Akaike’s information criterion

ALS:

Amyotrophic lateral sclerosis

APC model:

Age-period-cohort model

ICD:

International classification of diseases

MND:

Motor neuron disease

SMR:

Standardized mortality ratio

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Authors and Affiliations

  1. INSERM, U708, 75013, Paris, France

    Paul H. Gordon, Fanny Artaud & Alexis Elbaz

  2. UPMC Univ Paris 06, UMR_S 708, 75005, Paris, France

    Paul H. Gordon, Fanny Artaud, Vincent Meininger & Alexis Elbaz

  3. Inserm-CépiDc, Le Vésinet, France

    Albertine Aouba & Françoise Laurent

  4. Fédération des Maladies du Système Nerveux, AP-HP, Hôpital de la Salpétrière, Paris, France

    Paul H. Gordon & Vincent Meininger

  5. INSERM, Unité U708, Neuroépidémiologie, Hôpital de la Pitié-Salpêtrière, 47 Boulevard de l’Hôpital, 75651, Paris Cedex 13, France

    Paul H. Gordon

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  1. Paul H. Gordon
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  2. Fanny Artaud
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Correspondence to Paul H. Gordon.

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Appendix: age-period-cohort models

Appendix: age-period-cohort models

The mortality rates λ ijk for age i, period j, and cohort k (k = j − i) are λ ijk  = m ijk /T ijk , where m ijk is the expected number of deaths and T ijk the number of person-years for the ijk combination. The observed number of deaths, n ijk , is considered to arise from a Poisson distribution with mean m ijk . Age-period-cohort models assume that the three time factors, age, period, and cohort have an additive effect on the logarithm of the rates and can be generally written as log λ ijk  = μ + α i  + β j  + γ k , where age effects are represented by α i , period effects by β j , and cohort effects by γ k . Poisson regression is used to model mortality rates using person-years as an offset.

Clayton and Schifflers [17, 18] have proposed a sequential procedure (supplementary figure 1). Models of increasing complexity, depending on which terms are included in the regression model, are sequentially fitted to the data:

  • Model 1: Age

  • Model 2: Age-Drift (the drift parameter fits a simple linear trend in mortality with time)

  • Model 3: Age-Period

  • Model 4: Age-Cohort

  • Model 5: Age-Period-Cohort

Model 1 assesses whether mortality changes with age. Model 2 evaluates whether there are additional linear trends that cannot ascribed to period or cohort effects. If this model does not fit adequately the data, then more complex models can be evaluated. Model 3 evaluates the presence of period effects and model 4 tests for cohort effects. If none of these models fits the data adequately, model 5, which includes age, period, and cohort effects, is fitted.

The goodness-of-fit of the models is assessed through ratios of deviance to the degrees of freedom; ratios that are closer to one indicate a better fit. A lower value of the Akaike’s information criterion (AIC) also indicates a better fit. Models can be compared using the difference between their deviances; under the null hypothesis, this difference follows a chi-square distribution whose degrees of freedom are computed as the difference between degrees of freedom from the two models that are compared.

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Gordon, P.H., Artaud, F., Aouba, A. et al. Changing mortality for motor neuron disease in France (1968–2007): an age-period-cohort analysis. Eur J Epidemiol 26, 729–737 (2011). https://doi.org/10.1007/s10654-011-9595-0

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  • DOI: https://doi.org/10.1007/s10654-011-9595-0

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