Emerging objectives and methods in epidemiology.

Koopman, Jim

doi:10.2105/ajph.86.5.630

Cited by 66 publications

(28 citation statements)

References 9 publications

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“…This ground has been defined in various ways. Fundamentally, there is a hope that researchers might look into the 'black box' of causality (Koopman, 1996) and that a fuller 'causative chain' could be envisaged (Susser, 1996). Any tendency to see these complex variables as 'confounders' (for example, in this work, 'social context') could thus be eschewed (Pearce, 1996).…”

Section: Moving Onmentioning

confidence: 90%

The physical activity – mental wellbeing association in young people: A case study in dealing with a complex public health topic using a ‘realistic evaluation’ framework

Whitelaw

Teuton

Swift³

et al. 2010

Mental Health and Physical Activity

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Section: Moving Onmentioning

confidence: 90%

The physical activity – mental wellbeing association in young people: A case study in dealing with a complex public health topic using a ‘realistic evaluation’ framework

Whitelaw

Teuton

Swift³

et al. 2010

Mental Health and Physical Activity

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“…A common critique of standard epidemiological approaches in infectious-disease research is that studies do not account for dependencies between outcomes for individuals [14,54,55]. Dependencies can occur in several ways.…”

Section: Accounting For and Modeling Dependenciesmentioning

confidence: 99%

“…Approaches based on transmission dynamics study the changes that occur in a dynamic system (a population) when features of the system change. Patterns of disease in populations, as opposed to the determinants of disease in individuals, are the main focus of interest [54,55]. Mathematical models of the transmission dynamics of infectious diseases (based on different scenarios regarding, for example, contact patterns and transmission probabilities) can be used to estimate how a specific change at the population level modifies the dynamics of transmission of disease and, hence, the incidence or prevalence of disease [59,60].…”

Section: Modeling Dynamic Relationshipsmentioning

confidence: 99%

“…The appropriateness of multilevel analysis depends on the question of interest. Strategies that directly model the nonlinear dynamics of transmission systems may be more appropriate for research questions that pertain to predictors of changes in patterns of disease in populations over time [54]. Both methods can be thought of as complementary.…”

Section: Modeling Dynamic Relationshipsmentioning

confidence: 99%

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Multilevel Analysis of Infectious Diseases

Roux

Aiello

2005

J INFECT DIS

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Traditional study designs, such as individual-level studies and ecological studies, are unable to simultaneously examine the effects of individual-level and group-level factors on risk of disease. Multilevel analysis overcomes this limitation by allowing the simultaneous investigation of factors defined at multiple levels. Areas in which multilevel modeling can be applied to sexually transmitted infection (STI) research include examining how both group-level and individual-level factors are related to individual-level STI outcomes, assessing interactions between individual-level and group-level constructs, and exploring how factors at multiple levels contribute to group-to-group differences in rates of disease. In this article, we review the fundamentals of multilevel modeling, the applications of multilevel models for the examination of STIs, and the key challenges associated with using multilevel modeling for infectious-disease research.Over the past few decades, epidemiological studies have focused, for the most part, on the identification of individual-level risk factors for disease. The underlying assumption in this approach has been that the causes of disease can be found at the level of individuals. This individual-centered approach has been reflected in behavioral and biomedical models of disease causation and reemerges today in the notion that genetic factors play a major role in the etiology of disease. Populations are usually viewed as collections of individuals, rather than as meaningful entities with inherent properties that may be related to the likelihood that individuals within them develop disease. Recently in epidemiology, however, interest has been increasing in recovering the population or group dimension and in reconsidering the types of variables, types of study designs, and types of analytical approaches needed to develop explanations of the causes of disease that incorporate individual-level and population-level factors [1][2][3][4]. The growing consensus is that, with regard to both scientific validity and the practical implications for prevention of disease, in- vestigations of the causes of diseases need to include factors defined at multiple levels. The importance of multilevel determinants has been especially highlighted in recent reviews of the epidemiology of sexually transmitted infections (STIs), for which the influence of population-level factors has long been recognized [5][6][7][8].Multilevel analysis recently has emerged as an analytical strategy that may be useful in incorporating factors defined at multiple levels in epidemiological analyses [9][10][11]. This article will review the role of population-level factors in infectious-disease epidemiology, summarize the fundamentals of the multilevel approach, and briefly review recent applications of this approach in infectious-disease and STI research. We conclude with a discussion of the strengths and limitations of multilevel analysis in the study of infectious diseases and the challenges raised by its use. POPULATION-LEVEL (...

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“…[1][2][3][4][5] Although reference to ecology in these health arenas is a comparatively recent phenomenon, giving it the appearance of a ''new'' perspective, the ecological approach has roots in several disciplines dating back more than a century. 6 Scholars argue that ecology has always been a heterogeneous field, which has not one, but many histories corresponding to diverse scientific, philosophical, and sociopolitical contexts.…”

mentioning

confidence: 99%