Sreeramareddy et al. BMC Public Health 2011, 11:403 http://www.biomedcentral.com/1471-2458/11/403 leading to exposure misclassification. Type of fuel used during pregnancy may have been different from that being used at the time of survey. The main outcome i.e. birth weight was not available for majority of births. The use of size of baby at birth as proxy to birth weight has lead to misclassification bias. Mothers were generally very good at identifying non-LBW babies. Only about half of the babies whose birth weight was reported or recorded as below 2500 grams were correctly classified as less than average size at birth (i.e. LBW). This low sensi- tivity of outcome measure may have lead to attenuation of association (effect size) in our final multivariable ana- lysis. The use of actual birth weight as recorded from child health card and/or mother’s recall as outcome mea- sure would have led to selection bias (a large proportion of babies were not weighed in high pollution fuels com- pared to low pollution fuels i.e. 71% versus. 26.1%). Simi- larly, women from more affluent households, with better education were likely to have recorded birth weight. Use of birth weight as recorded from child health card may also lead to misclassification bias. This is because birth weight recording in developing country like India is usually imprecise except for hospital births. In India, where are high proportion of births are taking place at home, weight is measured at a convenient time. We did not have information about some important determi- nants of birth weight from the DHS data i.e. foods con- sumed and physical activity, and exposure to second hand smoke during pregnancy. We could not examine the effect of these variables on size at birth in multivari- able analysis. We could not assign the households using combination of fuels into a different category since we did not have such information from DHS data. We lacked information about duration of exposure, and stage of pregnancy when exposure had occurred. The women exposed to biomass smoke may have had higher rates of still birth [23] and miscarriages. Since we included only live births, the pro- portion of pregnancies that may have lead to LBW babies is not known. Our effect size might have been an under- estimate due to the reasons mentioned above. The data on birth weight was collected for previous five years and during this period the households might have switched from high pollution (biomass) fuels to cleaner fuels for cooking. We did not have the data about this to carry out a separate analysis. The birth weight and size at birth we analysed were for most recent births during five years prior to the date of survey (index pregnancy). Some of the confounding variables we used in our multivariable analysis i.e. haemoglobin, BMI, smoking were done dur- ing the time of survey. However, these measurements may not have been the same during the index pregnancy to which they were applied. Therefore, our results should be interpreted with caution. Page 9 of 10 Despite these limitations, the consistency of the asso- ciation from various studies and systematic reviews [23] provides a strong case for public health policy and pro- grams to reduce exposure to smoke from biomass fuel. As exposure to smoke from biomass fuel is known to cause other health effects [16,33,34] as well, public information campaigns about health risks of exposure should be given. In areas where shifting to cleaner fuels is not feasible due to financial reasons, well designed stoves to improve combustion and ventilation by use of chimneys (an outlet for smoke) should be promoted. Conclusion Use of biomass fuels is associated with child size at birth. Our results are consistent with previous observa- tional studies in different developing country settings. Future studies should investigate this association using robust study designs which use more direct methods for measuring level of exposure to smoke emitted from burning biomass fuels and clinical parameters like birth weight and other pregnancy outcomes. Acknowledgements We thank the ORC Macro international for providing us the data to carry out this analysis and prepare this manuscript. We thank Dr. N Sreekumaran Nair Department of statistics, Manipal University for his advice while carrying out statistical analysis. Author details 1Associate Professor, Department of Community Medicine, Melaka-Manipal Medical College, Jalan Batu Hampar, Bukit Baru, Melaka, Malaysia. 2Lecturer, Indian Institute of Public Health, Vengalrao Nagar, Hyderabad, India. 3Professor, Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA. Authors’ contributions CTS conceptualised the study, wrote the project proposal and write the first draft of manuscript. RRS assisted in conceptualisation, carried out data analysis, interpretation of results and commented on draft manuscript. NS assisted in conceptualisation, assisted in data analysis and commented on draft manuscript. All the authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Received: 16 November 2010 Accepted: 27 May 2011 Published: 27 May 2011 References 1. 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