Dear Editor

Some forms of maternal morbidity may not kill mothers, but have the ability to incapacitate them, enough to make them vertually non-existent, as far as their infant is concerned e.g. Post Partum psychosis/depression is one such, which is quite commonly observed in many mothers following delivery, understood to be triggered by interplay of hormones due to pregnancy related physiology during and immediately after delivery.

Although the mother may be afflicted with the depression, but the equal sufferer and unfortunate victim is ultimately her infant. The mother in this condition, may refuse to breast feed her new born and may ignore or even dis-own her kid. Even if mother is willing to breast feed, the newborn is taken off from feeding, if the mother is put on anti-depressants. All of which deprives the new born of crucial nourishment and more importantly the bonding in the initial weeks of birth and will have to contend with external feed. Not to speak of tremendous damage done due to loss of mother-child bonding and its after effects.

The above illustration is just to emphasise that the maternal morbidity is very intimately linked with the infant’s morbidity n health, therefore prompt and early public health intervention aimed at ante partum and post partum partum depression during post partum period and thereafter, will help not only in improving maternal health but also will have a bearing on improving Infant health and hence, indirectly but positively influencing both Maternal Mortality Rate and Infant Mortality Rate.

Most data on ethnic inequalities in health will have studied ‘first- generation’ migrants from South Asia, as even the oldest second-generation South Asians are only now in 2009 beginning to enter their 40s.

Smith et al’s[1] pertinent question is to ask whether the sons and daughters of first-generation South Asians will be at equal risk of poor health. The case of coronary disease is of particular importance considering the higher rate ratios for coronary mortality in men and women from South Asia.[2-4] There is however conflicting evidence in studies examining acculturation. Decreasing mortality with increasing duration of residence in the new host country was observed for migrants from South Asia in a study in a study in Australia,[5] perhaps due to cardio-protective behavioural practices in the Australia. However, studies in the UK have presented opposite results to the Australian work, with increasing cardiovascular risk with length of residence among South Asian migrants.[6]

The interaction of behavioural practices and socio-economic patterns will complicate matters. The coronary risk of a group of educated and affluent second-generation South Asians may more resemble that of a similarly educated and affluent white group, as opposed to a less educated group and less affluent group of second-generation South Asians. It is here that perhaps the heterogeneity of the South Asian group may become increasingly important in the future. It is interesting that apart from Indians, minority ethnic groups (including Pakistani/Bangladeshis) are found to be more likely to engage in poor dietary behaviours in a study of adolescent and parental lifestyles, with those born in the UK and girls being more susceptible.[7] Thus, some subgroups may inherit (more so through their behaviour than their genes) the diseases of their parents, whilst others – like the offspring of some professional Indian classes – inherit the disease profile of the majority white population.

Beyond socio-economic mechanisms, the influence of biology may continue to adversely affect the children of South Asians. South Asians have a smaller superficial subcutaneous adipose tissue compartment than white people and a theory has been proposed that babies born to mothers of South Asian descent are smaller and have less peripheral fat, and that during subsequent growth and development immersed in the richer diet of the developed world, this primary compartment reaches its capacity for fat storage rapidly and the deep subcutaneous and visceral compartments become more prominent, with adverse consequences for risks of diabetes and coronary disease.[8] Early evidence of ethnic differences in coronary risk has also been presented in a cross sectional comparison of British South Asian and white children, with ethnic differences being present even in these children aged 8 to 11 years – an increased tendency to insulin resistance was observed in South Asian children, and the authors inferred an increased sensitivity to adiposity as a result.[9]

These findings that second-generation minority ethnic groups in England continue to report as poor a general health as their parents, unaffected by changes in health behaviours,[1] suggests that continuing investigation into ethnic inequalities in health – and into both biological and socio-economic determinants - will be needed in the progeny of first-generation migrants.

References

1. Smith NR, Kelly YJ, Nazroo JY. Intergenerational continuities of ethnic inequalities in general health in England. J Epidemiol Community Health 2009;63(March 1, 2009):253-258.

2. Harding S, Rosato M, Teyhan A. Trends for coronary heart disease and stroke mortality among migrants in England and Wales, 1979-2003: slow declines notable for some groups. Heart 2008;94(4):463-70.

3. Wild SH, Fischbacher C, Brock A, Griffiths C, Bhopal R. Mortality from all causes and circulatory disease by country of birth in England and Wales 2001-2003. J Public Health (Oxf) 2007;29(2):191-8.

4. Fischbacher CM, Steiner M, Bhopal R, Chalmers J, Jamieson J, Knowles D, et al. Variations in all cause and cardiovascular mortality by country of birth in Scotland, 1997-2003. Scott Med J 2007;52(4):5-10.

5. Gray L, Harding S, Reid A. Evidence of divergence with duration of residence in circulatory disease mortality in migrants to Australia. Eur J Public Health 2007;17(6):550-4. 6. Harding S. Mortality of migrants from the Indian subcontinent to England and Wales: effect of duration of residence. Epidemiology 2003;14(3):287-92.

7. Harding S, Teyhan A, Maynard MJ, Cruickshank JK. Ethnic differences in overweight and obesity in early adolescence in the MRC DASH study: the role of adolescent and parental lifestyle. Int. J. Epidemiol. 2008;37(February 1, 2008):162-172.

8. Sniderman AD, Bhopal R, Prabhakaran D, Sarrafzadegan N, Tchernof A. Why might South Asians be so susceptible to central obesity and its atherogenic consequences? The adipose tissue overflow hypothesis. Int J Epidemiol 2007;36(1):220-5.

9. Lampe FC, Morris RW, Walker M, Shaper AG, Whincup PH. Trends in rates of different forms of diagnosed coronary heart disease, 1978 to 2000: prospective, population based study of British men. BMJ 2005;330(7499):1046-.

Editor - An improved understanding of the role of place for health is one of the main challenges in social epidemiology [1]. To contribute to this challenge, many studies are concerned with the identification of environmental characteristics related to health. As a consequence of the early phase of research in this field, a majority of these studies employ a cross-sectional design. Recent papers in the Journal have described how methodological advances can contribute to understanding the role of place for health, putting emphasis on improving causal inference [2, 3]. Without denying the crucial importance of these methodological advances, studies should also be grounded on theory about how environmental characteristics are interrelated and temporally connected to each other. In absence of clearly expressed thoughts, a recent study reported “little evidence that better locational access to tobacco retail in New Zealand is associated with individual-level smoking behaviour [4]”. A crucial element in reaching this conclusion is an adjustment for neighbourhood-level deprivation: before the adjustment, those with best access to supermarkets and to convenience stores showed an increased probability of being a smoker, but odds ratios attenuated to the null after the adjustment with confidence intervals no longer including 1. The adjustment was justified by the finding that deprived neighbourhoods may have a disproportionate number of outlets selling tobacco, and that smoking is more prevalent in deprived neighbourhoods. Thus, neighbourhood deprivation was conceptualized as a classical confounder for which one needs to adjust in order to gain insight into the influence of “retail availability, other things being equal”. However, it is likely that owners of retail outlets know and want to be close to where most clients are, and that the disproportionate number of outlets in deprived neighbourhoods is due to the level of deprivation. To the extent this is true, it would have been justified not to adjust for neighbourhood deprivation. This results in a substantially different conclusion: better locational access to tobacco retail provision is associated with individual-level smoking behaviour in New Zealand.

References

1. Kaplan GA. What's wrong with social epidemiology, and how can we make it better? Epidemiol Rev 2004;26:124-35.

2. Fleischer NL, Diez Roux AV. Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction. J Epidemiol Community Health 2008;62:842-6.

3. Kawachi I, Subramanian SV. Neighbourhood influences on health. J Epidemiol Community Health 2007;61:3-4. 4. Pearce J, Hiscock R, Moon G, et al. The neighbourhood effects of geographical access to tobacco retailers on individual smoking behaviour. J Epidemiol Community Health 2009;63:69-77.

Proposed obesity body mass index correction for self-reported data may not be appropriate

David Faeh and Matthias Bopp

Institute of Social and Preventive Medicine (ISPM), University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland

The study published by Dauphinot et al. in the February issue of the Journal of Epidemiology and Community Health compared self-reported and measured BMI in a sample of the general population of Geneva (Switzerland) and a smaller French sample [1]. In order to account for underestimation of BMI arising from self-reports, the authors proposed an universal cut- off of 29.2 kg/m2 instead of 30 kg/m2 for the definition of obesity. In a previous study, we compared the obesity prevalence based on measured and self-reported BMI in ten population-based studies carried out in Switzerland between 1977 and 2003 (N=54,315) [2]. As shown in the figure, we found considerably higher (1.6 times) prevalence of obesity in the five studies using measured BMI (black solid line and squares) compared to the five surveys with self-reports (grey solid line triangles, for details see [2]). The use of the Dauphinot cut-off in our data suggests that this could indeed improve estimates of obesity. However, the proposed cut-off of 29.2 kg/m2 (dotted line, diamonds) may not sufficiently account for self-report bias and for sex-differences. The latter is supported by figure 1 in the Dauphinot publication and by the majority of population studies included in the review of Connor Gorber et. al [3]. According to our data, a cut-off of 28.6 kg/m2 in men and 28.2 kg/m2 in women (dashed line, crosses) could be more appropriate. Probably, persons participating in the Geneva study have a lower body weight than inhabitants of other Swiss areas. For example, in the CoLaus population based study carried out in Lausanne, the obesity prevalence was 15.6% (vs 12.6% in the Geneva population).[1, 4] Furthermore the validation with the French study may be problematic, since the BMI underestimation mainly arose from an overestimation of height in that population, while it predominantly resulted from an underestimation of weight in the sample from Geneva.[1] Finally, it is questionable whether an ubiquitary valid BMI threshold can be derived. This is also suggested by the rather large differences between studies (see [3]) and by the fact that rounding of height strongly depends on cultural affiliation.[5] Nevertheless, all lines derived from linear regression are quite parallel (particularly in men) suggesting that a single threshold for self-reported BMI works independently of time.

Figure. Mean (%) prevalence of obesity with trend line (obteined with linear regression) by study.

References

1. Dauphinot V, Wolff H, Naudin F, et al. New obesity body mass index threshold for self-reported data. J Epidemiol Community Health 2009;63(2):128-32. 2. Faeh D, Marques-Vidal P, Chiolero A, et al. Obesity in Switzerland: do estimates depend on how body mass index has been assessed? Swiss Med Wkly 2008;138(13-14):204-10. 3. Connor Gorber S, Tremblay M, Moher D, et al. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev 2007;8(4):307-26. 4. Firmann M, Mayor V, Vidal PM, et al. The CoLaus study: a population- based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord 2008;8:6. 5. Bopp M, Faeh D. Who gives me five? Rounding preference for self reported height depends on language. BMJ 2008;337(a2950):1463.