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      International migration and adverse birth outcomes: role of ethnicity, region of origin and destination

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          The literature on international migration and birth outcomes shows mixed results. This study examined whether low birth weight (LBW) and preterm birth differed between non-migrants and migrant subgroups, defined by race/ethnicity and world region of origin and destination.


          A systematic review and meta-regression analyses were conducted using three-level logistic models to account for the heterogeneity between studies and between subgroups within studies.


          Twenty-four studies, involving more than 30 million singleton births, met the inclusion criteria. Compared with US-born black women, black migrant women were at lower odds of delivering LBW and preterm birth babies. Hispanic migrants also exhibited lower odds for these outcomes, but Asian and white migrants did not. Sub-Saharan African and Latin-American and Caribbean women were at higher odds of delivering LBW babies in Europe but not in the USA and south-central Asians were at higher odds in both continents, compared with the native-born populations.


          The association between migration and adverse birth outcomes varies by migrant subgroup and it is sensitive to the definition of the migrant and reference groups.

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          Most cited references 89

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          Meta-analysis of Observational Studies in EpidemiologyA Proposal for Reporting

           Donna Stroup (2000)
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            How should meta-regression analyses be undertaken and interpreted?

            Appropriate methods for meta-regression applied to a set of clinical trials, and the limitations and pitfalls in interpretation, are insufficiently recognized. Here we summarize recent research focusing on these issues, and consider three published examples of meta-regression in the light of this work. One principal methodological issue is that meta-regression should be weighted to take account of both within-trial variances of treatment effects and the residual between-trial heterogeneity (that is, heterogeneity not explained by the covariates in the regression). This corresponds to random effects meta-regression. The associations derived from meta-regressions are observational, and have a weaker interpretation than the causal relationships derived from randomized comparisons. This applies particularly when averages of patient characteristics in each trial are used as covariates in the regression. Data dredging is the main pitfall in reaching reliable conclusions from meta-regression. It can only be avoided by prespecification of covariates that will be investigated as potential sources of heterogeneity. However, in practice this is not always easy to achieve. The examples considered in this paper show the tension between the scientific rationale for using meta-regression and the difficult interpretative problems to which such analyses are prone. Copyright 2002 John Wiley & Sons, Ltd.
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              Controlling the risk of spurious findings from meta-regression.

              Meta-regression has become a commonly used tool for investigating whether study characteristics may explain heterogeneity of results among studies in a systematic review. However, such explorations of heterogeneity are prone to misleading false-positive results. It is unclear how many covariates can reliably be investigated, and how this might depend on the number of studies, the extent of the heterogeneity and the relative weights awarded to the different studies. Our objectives in this paper are two-fold. First, we use simulation to investigate the type I error rate of meta-regression in various situations. Second, we propose a permutation test approach for assessing the true statistical significance of an observed meta-regression finding. Standard meta-regression methods suffer from substantially inflated false-positive rates when heterogeneity is present, when there are few studies and when there are many covariates. These are typical of situations in which meta-regressions are routinely employed. We demonstrate in particular that fixed effect meta-regression is likely to produce seriously misleading results in the presence of heterogeneity. The permutation test appropriately tempers the statistical significance of meta-regression findings. We recommend its use before a statistically significant relationship is claimed from a standard meta-regression analysis. Copyright 2004 John Wiley & Sons, Ltd.

                Author and article information

                [1 ]Centre for Research on Inner City Health, St. Michael's Hospital, Toronto, Canada
                [2 ]Institute for Clinical Evaluative Sciences, Toronto, Canada
                [3 ]Epidemiological Research Unit on Perinatal Health and Women's Health (INSERM), Paris, France
                [4 ]National Research and Development Centre for Welfare and Health (STAKES), Helsinki, Finland
                [5 ]City University, London, UK
                [6 ]Statistics Canada, Ottawa, Canada
                [7 ]Faculty of Nursing, University of Manitoba, Winnipeg, Canada
                [8 ]University of Oslo, Rikshospitalet, Oslo, Norway
                [9 ]McGill University/MUHC, Montreal, Canada
                Author notes
                Correspondence to Dr Marcelo Luis Urquia, Centre for Research on Inner City Health, St Michael's Hospital, 70 Richmond Street E, 4th Floor, Toronto, ON M5C 1N8, Canada; marcelo.urquia@

                See end of paper for members of the ROAM collaboration.

                J Epidemiol Community Health
                Journal of Epidemiology and Community Health
                BMJ Group (BMA House, Tavistock Square, London, WC1H 9JR )
                4 March 2010
                March 2010
                4 March 2010
                : 64
                : 3
                : 243-251
                © 2009, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: and

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