A nationwide survey of the influence of month of birth on the risk of developing multiple sclerosis in Sweden and Iceland

Previous studies have shown that the risk of multiple sclerosis (MS) is associated with season of birth with a higher proportion of MS patients being born in spring. However, this relationship has recently been questioned and may be due to confounding factors. Our aim was to assess the influence from season or month of birth on the risk of developing MS in Sweden and Iceland. Information about month of birth, gender, and phenotype of MS for patients born 1940–1996 was retrieved from the Swedish MS registry (SMSR), and their place of birth was retrieved from the Swedish Total Population Registry (TPR). The corresponding information was retrieved from medical journals of Icelandic MS patients born 1981–1996. The control groups consisted of every person born in Sweden 1940–1996, their gender and county of birth (TPR), and in Iceland all persons born between 1981 and 1996 and their gender (Statistics Iceland). We calculated the expected number of MS patients born during each season and in every month and compared it with the observed number. Adjustments were made for gender, birth year, and county of birth. We included 12,020 Swedish and 108 Icelandic MS patients in the analyses. There was no significant difference between expected and observed MS births related to season or month of birth in Sweden or Iceland. This was even the results before adjustments were made for birth year and birth place. No significant differences were found in subgroup analyses including data of latitude of birth, gender, clinical phenotype, and MS onset of 30 years or less. Our results do not support the previously reported association between season or month of birth and MS risk. Analysis of birth place and birth year as possible confounding factors showed no major influence of them on the seasonal MS risk in Sweden and Iceland

Система дистанційної освіти та її захист

BACKGROUND: It is currently unknown whether early immunomodulatory treatment in relapsing-remitting MS (RRMS) can delay the transition to secondary progression (SP). OBJECTIVE: To compare the time interval from onset to SP in patients with RRMS between a contemporary cohort, treated with first generation disease modifying drugs (DMDs), and a historical control cohort. METHODS: We included a cohort of contemporary RRMS patients treated with DMDs, obtained from the Swedish National MS Registry (disease onset between 1995-2004, n = 730) and a historical population-based incidence cohort (onset 1950-64, n = 186). We retrospectively analyzed the difference in time to SP, termed the "period effect" within a 12-year survival analysis, using Kaplan-Meier and Cox regression analysis. RESULTS: We found that the "period" affected the entire severity spectrum. After adjusting for onset features, which were weaker in the contemporary material, as well as the therapy initiation time, the DMD-treated patients still exhibited a longer time to SP than the controls (hazard ratios: men, 0.32; women, 0.53). CONCLUSION: Our results showed there was a longer time to SP in the contemporary subjects given DMD. Our analyses suggested that this effect was not solely driven by the inclusion of benign cases, and it was at least partly due to the long-term immunomodulating therapy given

Stochasticity in the adaptive dynamics of evolution: The bare bones

First a population model with one single type of individuals is considered. Individuals reproduce asexually by splitting into two, with a population-size-dependent probability. Population extinction, growth and persistence are studied. Subsequently the results are extended to such a population with two competing morphs and are applied to a simple model, where morphs arise through mutation. The movement in the trait space of a monomorphic population and its possible branching into polymorphism are discussed. This is a first report. It purports to display the basic conceptual structure of a simple exact probabilistic formulation of adaptive dynamics

Estimating the within-household infection rate in emerging SIR epidemics among a community of households

This paper is concerned with estimation of the within household infection rate λL for a susceptible → infective → recovered epidemic among a population of households, from observation of the early, exponentially growing phase of an epidemic. Specifically, it is assumed that an estimate of the exponential growth rate is available from general data on an emerging epidemic and more-detailed, household-level data are available in a sample of households. Estimates of λL obtained using the final size distribution of single-household epidemics are usually biased owing to the emerging nature of the epidemic. A new method, which accounts correctly for the emerging nature of the epidemic, is developed by exploiting the asymptotic theory of supercritical branching processes and proved to yield a strongly consistent estimator of λL as the population and sampled households both tend to infinity in an appropriate fashion. The theory is illustrated by simulations which demonstrate that the new method is feasible for finite populations and numerical studies are used to explore how changes to the parameters governing the spread of an epidemic affect the bias of estimates based on single-household final size distributions

On stationary Markov chains and independent random variables

Two new proofs are given for the fact that a stationary, irreducible, aperiodic Markov chain (Xn N = ..., -1,0,1,2...) with denumerable state space has a representation of the form X'n=g(Un-1, Un-2,...), where g is a measurable function, (Un, N= ..., -1,0,1,2,...) a sequence of independent random variables uniformly distributed on (0,1), and (X'n) has the same probability law as (Xn).Markov chain representation i.i.d. random variables

Multi-type branching in varying environment

This paper considers the asymptotic theory of the varying environment Galton-Watson process with a countable set of types. This paper examines the convergence in Lp and almost surely of the numbers of the various types when normalised by the corresponding expected number. The harmonic functions of the mean matrices play a central role in the analysis. Many previously studied models provide particular cases.Harmonic function Matrix products Martingale Inhomogeneous Markov chains Lp convergence

Historical HbA1c values may explain the type 2 diabetes legacy effect: UKPDS 88

Type 2 diabetes all-cause mortality (ACM) and myocardial infarction (MI) glycemic legacy effects have not been explained. We examined their relationships with prior individual HbA1c values and explored the potential impact of instituting earlier, compared with delayed, glucose-lowering therapy. Twenty-year ACM and MI hazard functions were estimated from diagnosis of type 2 diabetes in 3,802 UK Prospective Diabetes Study participants. Impact of HbA1c values over time was analyzed by weighting them according to their influence on downstream ACM and MI risks. Hazard ratios for a one percentage unit higher HbA1c for ACM were 1.08 (95% CI 1.07-1.09), 1.18 (1.15-1.21), and 1.36 (1.30-1.42) at 5, 10, and 20 years, respectively, and for MI was 1.13 (1.11-1.15) at 5 years, increasing to 1.31 (1.25-1.36) at 20 years. Imposing a one percentage unit lower HbA1c from diagnosis generated an 18.8% (95% CI 21.1-16.0) ACM risk reduction 10-15 years later, whereas delaying this reduction until 10 years after diagnosis showed a sevenfold lower 2.7% (3.1-2.3) risk reduction. Corresponding MI risk reductions were 19.7% (22.4-16.5) when lowering HbA1c at diagnosis, and threefold lower 6.5% (7.4-5.3%) when imposed 10 years later. The glycemic legacy effects seen in type 2 diabetes are explained largely by historical HbA1c values having a greater impact than recent values on clinical outcomes. Early detection of diabetes and intensive glucose control from the time of diagnosis is essential to maximize reduction of the long-term risk of glycemic complications

Rat-mouse and rat-human comparative maps based on gene homology and high-resolution zoo-FISH

The laboratory rat, Rattus norvegicus, and the laboratory mouse, Mus musculus, are key animal models in biomedical research. A deeper understanding of the genetic interrelationsships between Homo sapiens and these two rodent species is desirable for extending the usefulness of the animal models. We present comprehensive rat-human and rat-mouse comparative maps, based on 1090 gene homology assignments available for rat genes. Radiation hybrid, FISH, and zoo-FISH mapping data have been integrated to produce comparative maps that are estimated to comprise 83-100% of the conserved regions between rat and mouse and 66-82% of the conserved regions between rat and human. The rat-mouse zoo-FISH analysis, supported by data for individual genes, revealed nine previously undetected conserved regions compared to earlier reports. Since there is almost complete genome coverage in the rat-mouse comparative map, we conclude that it is feasible to make accurate predictions of gene positions in the rat based on gene locations in the mouse.Comparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe