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      General and specific contextual effects in multilevel regression analyses and their paradoxical relationship: A conceptual tutorial


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          To be relevant for public health, a context (e.g., neighborhood, school, hospital) should influence or affect the health status of the individuals included in it. The greater the influence of the shared context, the higher the correlation of subject outcomes within that context is likely to be. This intra-context or intra-class correlation is of substantive interest and has been used to quantify the magnitude of the general contextual effect (GCE). Furthermore, ignoring the intra-class correlation in a regression analysis results in spuriously narrow 95% confidence intervals around the estimated regression coefficients of the specific contextual variables entered as covariates and, thereby, overestimates the precision of the estimated specific contextual effects (SCEs).

          Multilevel regression analysis is an appropriate methodology for investigating both GCEs and SCEs. However, frequently researchers only report SCEs and disregard the study of the GCE, unaware that small GCEs lead to more precise estimates of SCEs so, paradoxically, the less relevant the context is, the easier it is to detect (and publish) small but “statistically significant” SCEs. We describe this paradoxical situation and encourage researchers performing multilevel regression analysis to consider simultaneously both the GCE and SCEs when interpreting contextual influences on individual health.


          • The intra-context correlation is a measure of the general contextual effect (GCE).

          • Contextual measures of association inform on specific contextual effects (SCEs).

          • Many multilevel regression analyses only report SCEs.

          • Paradoxically, the lower the GCE the easier it is to detect “statistically significant” SCEs.

          • Multilevel regression analysis need to consider both GCEs and SCEs.

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          Most cited references19

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          Estimating causal effects from epidemiological data.

          In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods -- standardisation and inverse probability weighting -- to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.
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            The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology.

            The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.
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              Components of variance and intraclass correlations for the design of community-based surveys and intervention studies: data from the Health Survey for England 1994.

              The authors estimated components of variance and intraclass correlation coefficients (ICCs) to aid in the design of complex surveys and community intervention studies by analyzing data from the Health Survey for England 1994. This cross-sectional survey of English adults included data on a range of lifestyle risk factors and health outcomes. For the survey, households were sampled in 720 postal code sectors nested within 177 district health authorities and 14 regional health authorities. Study subjects were adults aged 16 years or more. ICCs and components of variance were estimated from a nested random-effects analysis of variance. Results are presented at the district health authority, postal code sector, and household levels. Between-cluster variation was evident at each level of clustering. In these data, ICCs were inversely related to cluster size, but design effects could be substantial when the cluster size was large. Most ICCs were below 0.01 at the district health authority level, and they were mostly below 0.05 at the postal code sector level. At the household level, many ICCs were in the range of 0.0-0.3. These data may provide useful information for the design of epidemiologic studies in which the units sampled or allocated range in size from households to large administrative areas.

                Author and article information

                SSM Popul Health
                SSM Popul Health
                SSM - Population Health
                19 May 2018
                August 2018
                19 May 2018
                : 5
                : 33-37
                [a ]Unit for Social Epidemiology, Department of Clinical Sciences, Faculty of Medicine, Lund University, CRC, Jan Waldenströms Street 35, SE- 214 21 Malmö, Sweden
                [b ]Center for Primary Health Care Research, Region Skåne, Malmö, Sweden
                [c ]Centre for Clinical Research Västmanland, Uppsala University, Västerås, Sweden
                [d ]Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
                [e ]Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [f ]Centre for Multilevel Modelling, University of Bristol, UK
                Author notes
                [* ]Corresponding author at: Unit of Social Epidemiology, Department of Clinical Sciences, Faculty of Medicine, Lund University, CRC, Jan Waldenströms Street 35, SE- 214 21 Malmö, Sweden. juan.merlo@ 123456med.lu.se
                © 2018 The Authors. Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                : 20 March 2018
                : 12 May 2018
                : 15 May 2018


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