Examining secular trends and seasonality in count data using dynamic generalized linear modelling: a new methodological approach illustrated with hospital discharge data on myocardial infarction

Lundbye‐Christensen, Søren; Dethlefsen, Claus; Gorst‐Rasmussen, Anders; Fischer, Thomas; Schønheyder, Henrik Carl; Rothman, Kenneth J.; Sørensen, Henrik Toft

doi:10.1007/s10654-009-9325-z

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…MS disease-modifying drugs (The Multiple Sclerosis Collaborative Research Group (MSCRG), 1996; Kappos et al, 2010; Polman et al, 2006) affect the BBB transfer rate to a variable extent because of differences in the characteristics of inflammatory infiltrates among anatomical areas of brain. Cytokines such as TNF-α and transforming growth factor-beta (TGF-β) affect tight junction protein expression and transfer rates of both brain barriers: BBB and blood–CSF barrier (Killestein et al, 2001; Lundbye-Christensen et al, 2009; Zhou et al, 2010). However, the extent and effects of MS drugs on brain barriers in the full-brain and in cortical GM are not fully understood.…”

Section: Discussionmentioning

confidence: 99%

Quantification of blood-to-brain transfer rate in multiple sclerosis

Taheri

Rosenberg

Ford

2013

Multiple Sclerosis and Related Disorders

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Blood–brain barrier (BBB) disruption visualized in lesions by MRI is a major biomarker of disease activity in multiple sclerosis (MS). However, in MS, destruction occurs to a variable extent in lesions as well as in gray matter (GM) and in the normal appearing white matter (NAWM). A method to quantify the BBB disruption in lesions as well as in non-lesion areas would be useful for assessment of MS progression and treatments. The objective of this study was to quantify the BBB transfer rate (Ki) in WM lesions, in the NAWM, and in the full-brain of MS patients. Thirteen MS patients with active lesions and 10 healthy controls with age and gender matching were recruited for full-brain and WM Ki studies. Dynamic contrast-enhanced MRI (DCEMRI) scans were conducted using T1 mapping with partial inversion recovery (TAPIR), a fast T1 mapping technique, following administration of a quarter-dose of the contrast agent Gadolinium-DTPA (Gd-DTPA). The Patlak modeling technique was used to derive a voxel-based map of Ki. In all patients contrast-enhanced lesions, quantified by Ki maps, were observed. Compared with controls, patients with MS exhibited an increase in mean Ki of the full-brain (P-value<0.05) but no significant difference in mean Ki of NAWM. The identified increase in full-brain Ki of MS patients suggests a global vascular involvement associated with MS disease. The lack of observed significant decrease in Ki in NAWM suggests lower involvement of WM vasculature than full-brain vasculature in MS. Ki maps constructed from time series data acquired by DCEMRI provide additional information about BBB that could be used for evaluation of vascular involvement in MS and monitoring treatment effectiveness.

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Section: Discussionmentioning

confidence: 99%

Quantification of blood-to-brain transfer rate in multiple sclerosis

Taheri

Rosenberg

Ford

2013

Multiple Sclerosis and Related Disorders

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“…All measurements are centrally checked by examination of the data including their ranges, distributions, means, standard deviations, outliers and logical errors. Data outliers and missing values are checked on the original forms and might be imputed and subsequent appropriate statisticial techniques are used [263][264][265][266][267][268][269][270][271][272][273][274]. The data of one specific measurement are only distributed for analyses after data The Generation R Study 831 collection and preparation is completed for that measurement for the whole cohort.…”

Section: Data Management and Privacy Protectionmentioning

confidence: 99%

The Generation R Study: design and cohort update 2012

et al. 2012

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The Generation R Study is a population-based prospective cohort study from fetal life until young adulthood. The study is designed to identify early environmental and genetic causes of normal and abnormal growth, development and health during fetal life, childhood and adulthood. The study focuses on four primary areas of research: (1) growth and physical development; (2) behavioural and cognitive development; (3) diseases in childhood; and (4) health and healthcare for pregnant women and children. In total, 9,778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. General follow-up rates until the age of 4 years exceed 75%. Data collection in mothers, fathers and preschool children included questionnaires, detailed physical and ultrasound examinations, behavioural observations, and biological samples. A genome wide association screen is available in the participating children. Regular detailed hands on assessment are performed from the age of 5 years onwards. Eventually, results forthcoming from the Generation R Study have to contribute to the development of strategies for optimizing health and healthcare for pregnant women and children.

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“…GLMs are a group of statistical models that provide a flexible approach in studies of seasonality, because they allow data to be fitted to various underlying mathematical functions [3]. A log‐linear Poisson regression model is a commonly used underlying function [21].…”

Section: Methods Used To Study Seasonal Variation In Infectious Diseasementioning

confidence: 99%

Methods to assess seasonal effects in epidemiological studies of infectious diseases—exemplified by application to the occurrence of meningococcal disease

Christiansen

Pedersen

Sørensen

et al. 2012

Clinical Microbiology and Infection

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Seasonal variation in occurrence is a common feature of many diseases, especially those of infectious origin. Studies of seasonal variation contribute to healthcare planning and to the understanding of the aetiology of infections. In this article, we provide an overview of statistical methods for the assessment and quantification of seasonality of infectious diseases, as exemplified by their application to meningococcal disease in Denmark in 1995-2011. Additionally, we discuss the conditions under which seasonality should be considered as a covariate in studies of infectious diseases. The methods considered range from the simplest comparison of disease occurrence between the extremes of summer and winter, through modelling of the intensity of seasonal patterns by use of a sine curve, to more advanced generalized linear models. All three classes of method have advantages and disadvantages. The choice among analytical approaches should ideally reflect the research question of interest. Simple methods are compelling, but may overlook important seasonal peaks that would have been identified if more advanced methods had been applied. For most studies, we suggest the use of methods that allow estimation of the magnitude and timing of seasonal peaks and valleys, ideally with a measure of the intensity of seasonality, such as the peak-to-low ratio. Seasonality may be a confounder in studies of infectious disease occurrence when it fulfils the three primary criteria for being a confounder, i.e. when both the disease occurrence and the exposure vary seasonally without seasonality being a step in the causal pathway. In these situations, confounding by seasonality should be controlled as for any confounder.

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Examining secular trends and seasonality in count data using dynamic generalized linear modelling: a new methodological approach illustrated with hospital discharge data on myocardial infarction

Cited by 13 publications

References 29 publications

Quantification of blood-to-brain transfer rate in multiple sclerosis

Quantification of blood-to-brain transfer rate in multiple sclerosis

The Generation R Study: design and cohort update 2012

Methods to assess seasonal effects in epidemiological studies of infectious diseases—exemplified by application to the occurrence of meningococcal disease

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