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      Birth cohort effects in neurological diseases: amyotrophic lateral sclerosis, Parkinson's disease and multiple sclerosis.

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          Generational differences in disease rates are the main subject of age-period-cohort (APC) analysis, which is mostly applied in cancer and suicide research. This study applied APC analysis to selected neurological diseases: amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD) and multiple sclerosis (MS).

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          Environmental risk factors for multiple sclerosis. Part I: the role of infection.

          Alberto Ascherio, Kassandra L Munger (2007)
          Although genetic susceptibility explains the clustering of multiple sclerosis (MS) cases within families and the sharp decline in risk with increasing genetic distance, it cannot fully explain the geographic variations in MS frequency and the changes in risk that occur with migration. Epidemiological data provide some support for the "hygiene hypothesis," but with the additional proviso for a key role of Epstein-Barr virus (EBV) in determining MS risk. We show that whereas EBV stands out as the only infectious agent that can explain many of the key features of MS epidemiology, by itself the link between EBV and MS cannot explain the decline in risk among migrants from high to low MS prevalence areas. This decline implies that either EBV strains in low-risk areas have less propensity to cause MS, or that other infectious or noninfectious factors modify the host response to EBV or otherwise contribute to determine MS risk. The role of infectious factors is discussed here; in a companion article, we will examine the possible role of noninfectious factors and provide evidence that high levels of vitamin D may have a protective role, particularly during adolescence. The primary purpose of these reviews is to identify clues to the causes of MS and to evaluate the possibility of primary prevention.
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            Environmental factors and multiple sclerosis.

            George C Ebers (2008)
            Studies in Canada have provided strong evidence that environmental factors act at a population level to influence the unusual geographical distribution of multiple sclerosis (MS). However, the available data accommodate more than one type of environmental effect. Migration studies show that changes to early environment can greatly affect risk, and there are recent indications that risk can be altered in situ. The rising incidence rates of MS in Canada implied by longitudinal increases in sex ratio place this effect in temporal context and narrow the candidates for mediating the effect of environment. Similarly, geographical patterns in Australia imply that modifiable environmental factors hold the key to preventing some 80% of cases. Genetic epidemiology provides overwhelming evidence that genetic background has an important complementary role. If genetic factors are held constant, the environment sets the disease threshold. Although these could be independent additive risk factors, it seems more likely that susceptibility is mediated by direct interactions between the environment and genes.
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              Understanding the effects of age, period, and cohort on incidence and mortality rates.

              T Holford (1991)
              Time trends for population-based disease rates often are summarized by using direct adjustment by period of diagnosis or death. Similarly, the effect of age often is presented graphically as age-specific rates for a given period of diagnosis. These approaches may be necessary if there is an absence of long-term data, as they provide a natural way for annually updating information when monitoring trends, or they may be a convenient way of summarizing a large amount of data (7, 10, 11, 39, 45). However, these summaries only can adjust for the effect of age in a given period; they implicitly ignore the cohort effect. The effect of cohort is an important factor in understanding time trends for many diseases. Thus, it is not advisable to use data analytic strategies that routinely ignore it. Another alternative to modeling is to give a graphical presentation of the age-specific rates themselves. As I noted in the introduction, some of the first analyses to identify the effect of cohort on diseases, such as tuberculosis and lung cancer, relied entirely on a graphical analysis. Although graphs certainly are an important part of the interpretation of time trends, it would be a mistake to limit your analysis to impressions of points on a graph. For example, such a perusal would not give an objective indication of the statistical significance of a particular pattern. Regression analysis forces us to recognize a fundamental problem with interpreting time trends in disease rates--a problem that you should remember, even when trying to understand a graphical display of time trends in age-specific rates.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Neuroepidemiology
                Title: Neuroepidemiology
                Publisher: S. Karger AG
                ISSN (Electronic): 1423-0208
                ISSN (Print): 0251-5350
                Publication date (Electronic): 2012
                Volume: 38
                Issue: 1
                Affiliations
                [1 ] Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland. k320256@ifspm.uzh.ch
                Article
                Publisher Item ID: 000334632
                DOI: 10.1159/000334632
                PubMed ID: 22236983
                SO-VID: acc55e4a-f299-4430-ad5d-a4fecc902682
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