Long-term trends in BMI: are contemporary childhood BMI growth references appropriate when looking at historical datasets?

Background Body mass index (BMI) is the most widely used surrogate measure of adiposity, and BMI z-scores are often calculated when comparing childhood BMI between populations and population sub-groups. Several growth references are currently used as the basis for calculation of such z-scores, for both contemporary cohorts as well as cohorts born decades ago. Due to the widely acknowledged increases in childhood obesity over recent years it is generally assumed that older birth cohorts would have lower BMIs relative to the current standards. However, this reasonable assumption has not been formally tested.   Methods Two growth references (1990 UK and 2000 CDC) are used to calculate BMI z-scores in three historical British national birth cohorts (National Survey of Health and Development (1958), National Child Development Study (1958) and British Cohort Study (1970)). BMI z-scores are obtained for each child at each follow-up age using the lambda-mu-sigma (LMS) method, and their distributions examined.   Results Across all three cohorts, median BMI z-score at each follow-up age is observed to be positive in early childhood. This is contrary to what might have been expected given the assumed temporal increase in childhood BMI. However, z-scores then decrease and become negative during adolescence, before increasing once more.   Conclusions The differences in BMI distribution between the historical cohorts and the contemporary growth references appear systematic and similar across the cohorts. This might be explained by contemporary reference data describing a faster tempo of weight increase relative to height than observed in older birth cohorts. Comparisons using z-scores over extended periods of time should therefore be interpreted with caution

The measurement of household socio-economic position in tuberculosis prevalence surveys: a sensitivity analysis.

OBJECTIVE: To assess the robustness of socio-economic inequalities in tuberculosis (TB) prevalence surveys. DESIGN: Data were drawn from the TB prevalence survey conducted in Lusaka Province, Zambia, in 2005-2006. We compared TB socio-economic inequalities measured through an asset-based index (Index 0) using principal component analysis (PCA) with those observed using three alternative indices: Index 1 and Index 2 accounted respectively for the biases resulting from the inclusion of urban assets and food-related variables in Index 0. Index 3 was built using regression-based analysis instead of PCA to account for the effect of using a different assets weighting strategy. RESULTS: Household socio-economic position (SEP) was significantly associated with prevalent TB, regardless of the index used; however, the magnitude of inequalities did vary across indices. A strong association was found for Index 2, suggesting that the exclusion of food-related variables did not reduce the extent of association between SEP and prevalent TB. The weakest association was found for Index 1, indicating that the exclusion of urban assets did not lead to higher extent of TB inequalities. CONCLUSION: TB socio-economic inequalities seem to be robust to the choice of SEP indicator. The epidemiological meaning of the different extent of TB inequalities is unclear. Further studies are needed to confirm our conclusions

The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?

Two recent articles, one by Vandenbroucke, Broadbent and Pearce (henceforth VBP) and the other by Krieger and Davey Smith (henceforth KDS), criticize what these two sets of authors characterize as the mainstream of the modern ‘causal inference’ school in epidemiology. The criticisms made by these authors are severe; VBP label the field both ‘wrong in theory’ and ‘wrong in practice’, and KDS—at least in some settings—feel that the field not only ‘bark[s] up the wrong tree’ but ‘miss[es] the forest entirely’. More specifically, the school of thought, and the concepts and methods within it, are painted as being applicable only to a very narrow range of investigations, to the exclusion of most of the important questions and study designs in modern epidemiology, such as the effects of genetic variants, the study of ethnic and gender disparities and the use of study designs that do not closely mirror randomized controlled trials (RCTs). Furthermore, the concepts and methods are painted as being potentially highly misleading even within this narrow range in which they are deemed applicable. We believe that most of VBP’s and KDS’s criticisms stem from a series of misconceptions about the approach they criticize. In this response, therefore, we aim first to paint a more accurate picture of the formal causal inference approach, and then to outline the key misconceptions underlying VBP’s and KDS’s critiques. KDS in particular criticize directed acyclic graphs (DAGs), using three examples to do so. Their discussion highlights further misconceptions concerning the role of DAGs in causal inference, and so we devote the third section of the paper to addressing these. In our Discussion we present further objections we have to the arguments in the two papers, before concluding that the clarity gained from adopting a rigorous framework is an asset, not an obstacle, to answering more reliably a very wide range of causal questions using data from observational studies of many different designs