EHD-04215; No of Pages 1 Early Human Development xxx (2015) xxx
Contents lists available at ScienceDirect
Early Human Development journal homepage: www.elsevier.com/locate/earlhumdev
Analysis of the sex ratio at birth in South Africa increased 9 months after the 2010 FIFA World Cup
I am writing regarding the analysis in the article Masukume, G., & Grech, V. (2015). The sex ratio at birth in South Africa increased 9 months after the 2010 FIFA World Cup. My concerns are threefold. For full reproducible detail showing my calculations see https://github.com/thoughtfulbloke/birthRateReanalysis.
result, only between the proportions in 2011 and the expected proportion of the aggregate samples. Losing the between sample differences made the p-value .08 more significant than if the samples had been tested in a multiple sample way that acknowledged the variation between samples (so the aggregation changed the p-value from 0.1 to 0.02). In addition, the rationale for aggregating all but the 2011 year was to remove the influence of any long term cycles in male/female birth ratios. This method has the opposite effect — it adds an appearance of significance equal to the height of 2011's point in the cycle relative to the average of the cycle.
1. The cohort
3. Removal of data
The FIFA World Cup was from June 11 to July 11 in 2010; the authors took the February and March 2011 birth figures as 9 months after the World Cup. However, taking the time from fertilisation and implantation of the embryo in the womb to delivery of 268 days with a standard deviation of 10 days (Jukic, et al. Length of human pregnancy and contributors to its natural variation. Human Reproduction, 2013) we can calculate the percentage of World Cup conceived children born in each month of 2011 (I also checked the South African records for evidence of any 2011 spike in the number of preterm births and could find no evidence to use a lower figure than 268). The births are centred on March/April not February/March. Regardless if one (March), two (March, April), or three (March, April, February) are chosen, if you choose the months with the most World Cup related births and calculated the results by the authors' method, the results are not statistically significant.
To quote from the article: “Because M/F shows significant cyclic temporal trends [9], analysis was restricted from 2008 to 2012 in order to minimize the effect of these trends. In addition, in order to take into account seasonality similar periods of selected years were compared, namely February and March. “ Gini [9] reported long term annual variation but no short term monthly or seasonal variation. Removing months and testing the data by comparison to years are counter to a strategy reflecting Gini's article. However, better yet is to test for the need to remove data. I did and seasonality is not a significant issue. There is no reason to exclude other months from the comparison.
2. Over-estimating the significance The authors' method takes 5 years of samples, aggregates those not in 2011 together to form a single sample of other selected years, and compares that with the 2011 data. This method destroys the variation between the samples in the non-2011 years. The comparison is, as a
http://dx.doi.org/10.1016/j.earlhumdev.2015.12.004 0378-3782/© 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article as: Hood D, , Early Hum Dev (2015), http://dx.doi.org/10.1016/j.earlhumdev.2015.12.004
David Hood Available online xxxx
Contents lists available at ScienceDirect
Early Human Development journal homepage: www.elsevier.com/locate/earlhumdev
Analysis of the sex ratio at birth in South Africa increased 9 months after the 2010 FIFA World Cup
I am writing regarding the analysis in the article Masukume, G., & Grech, V. (2015). The sex ratio at birth in South Africa increased 9 months after the 2010 FIFA World Cup. My concerns are threefold. For full reproducible detail showing my calculations see https://github.com/thoughtfulbloke/birthRateReanalysis.
result, only between the proportions in 2011 and the expected proportion of the aggregate samples. Losing the between sample differences made the p-value .08 more significant than if the samples had been tested in a multiple sample way that acknowledged the variation between samples (so the aggregation changed the p-value from 0.1 to 0.02). In addition, the rationale for aggregating all but the 2011 year was to remove the influence of any long term cycles in male/female birth ratios. This method has the opposite effect — it adds an appearance of significance equal to the height of 2011's point in the cycle relative to the average of the cycle.
1. The cohort
3. Removal of data
The FIFA World Cup was from June 11 to July 11 in 2010; the authors took the February and March 2011 birth figures as 9 months after the World Cup. However, taking the time from fertilisation and implantation of the embryo in the womb to delivery of 268 days with a standard deviation of 10 days (Jukic, et al. Length of human pregnancy and contributors to its natural variation. Human Reproduction, 2013) we can calculate the percentage of World Cup conceived children born in each month of 2011 (I also checked the South African records for evidence of any 2011 spike in the number of preterm births and could find no evidence to use a lower figure than 268). The births are centred on March/April not February/March. Regardless if one (March), two (March, April), or three (March, April, February) are chosen, if you choose the months with the most World Cup related births and calculated the results by the authors' method, the results are not statistically significant.
To quote from the article: “Because M/F shows significant cyclic temporal trends [9], analysis was restricted from 2008 to 2012 in order to minimize the effect of these trends. In addition, in order to take into account seasonality similar periods of selected years were compared, namely February and March. “ Gini [9] reported long term annual variation but no short term monthly or seasonal variation. Removing months and testing the data by comparison to years are counter to a strategy reflecting Gini's article. However, better yet is to test for the need to remove data. I did and seasonality is not a significant issue. There is no reason to exclude other months from the comparison.
2. Over-estimating the significance The authors' method takes 5 years of samples, aggregates those not in 2011 together to form a single sample of other selected years, and compares that with the 2011 data. This method destroys the variation between the samples in the non-2011 years. The comparison is, as a
http://dx.doi.org/10.1016/j.earlhumdev.2015.12.004 0378-3782/© 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article as: Hood D, , Early Hum Dev (2015), http://dx.doi.org/10.1016/j.earlhumdev.2015.12.004
David Hood Available online xxxx
