Bioelectromagnetics 35:201^209 (2014)
Maternal Residential Proximity to Sources of Extremely Low Frequency Electromagnetic Fields and Adverse Birth Outcomes in a UKCohort Frank deVocht,1* Kimberly Hannam,2 Philip Baker,3 and Raymond Agius1 1
Centre for Occupational and Environmental Health, Institute of Population Health, ManchesterAcademic Health Sciences Centre,The University of Manchester, Manchester, United Kingdom 2 Maternal and Fetal Health Research, Medical and Human Sciences, Manchester Academic Health Sciences Centre,The University of Manchester, Manchester, United Kingdom 3 Liggins Institute, University of Auckland, Auckland, New Zealand
Studies have suggested that exposure to extremely low frequency electromagnetic fields (ELF-EMF) may be associated with increased risk of adverse birth outcomes. This study tested the hypothesis that close proximity to residential ELF-EMF sources is associated with a reduction in birth weight and increased the risk of low birthweight (LBW), small for gestational age (SGA) and spontaneous preterm birth (SPTB). Closest residential proximity to high voltage cables, overhead power lines, substations or towers during pregnancy was calculated for 140356 singleton live births between 2004 and 2008 in Northwest England. Associations between proximity and risk for LBW, SGA and SPTB were calculated, as well as associations with birth weight directly. Associations were adjusted for maternal age, ethnicity, parity and for part of the population additionally for maternal smoking during pregnancy. Reduced average birth weight of 212 g (95% confidence interval (CI): 395 to 29 g) was found for close proximity to a source, and was largest for female births (251 g (95% CI: 487 to 15 g)). No statistically significant increased risks for any clinical birth outcomes with residential proximity of 50 m or less were observed. Living close (50 m or less) to a residential ELF-EMF source during pregnancy is associated with suboptimal growth in utero, with stronger effects in female than in males. However, only a few pregnant women live this close to high voltage cables, overhead power lines, substations or towers, likely limiting its public health impact. Bioelectromagnetics 35:201–209, 2014. © 2014 Wiley Periodicals, Inc. Key words: power lines; perinatal outcomes; birth outcomes; cohort study; birth weight
INTRODUCTION Suboptimal growth in utero has been linked to an increased risk of long-term health problems, including type 2 diabetes and cardiovascular disease (Barker hypothesis) [Barker, 2006]. It is unclear why, despite better provision of antenatal services in developed countries, rates of preterm birth (PTB) (birth prior to 27 weeks of completed gestation) are increasing [World Health Organisation (WHO), 2012] and rates of low birthweight (LBW) (100 200 >200 50 >50 100 >100 200 >200
6/83 9288/130590 21/341 9273/130332 60/1031 9234/129642 7/79 12201/126693 21/337 12187/126435 74/1010 12134/125762 7/77 7858/127867 18/331 7847/127613 65/996 7800/126948 7/77 8631/127094 21/328 8617/126843 73/988 8565/126183
1.37 (0.59–3.14) 1.00 1.09 (0.71–1.71) 1.00 1.00 (0.77–1.30) 1.00 1.27 (0.58–2.76) 1.00 0.85 (0.55–1.33) 1.00 0.97 (0.77–1.24) 1.00 1.73 (0.79–3.77) 1.00 1.00 (0.62–1.60) 1.00 1.16 (0.91–1.50) 1.00 1.56 (0.72–3.39) 1.00 1.06 (0.68–1.63) 1.00 1.18 (0.93–1.51) 1.00
4/31 2904/44007 9/127 2899/43911 20/369 2888/43669 5/29 3995/42613 10/124 3990/42518 27/359 3973/42283 3/31 2415/43459 6/127 2412/43363 18/362 2400/43128 3/31 2732/43142 8/125 125/43048 22/358 2713/42815
2.52 (0.88–7.26) 1.00 1.28 (0.65–2.53) 1.00 0.99 (0.63–1.57) 1.00 2.51 (0.95–6.61) 1.00 1.08 (0.56–2.07) 1.00 1.01 (0.68–1.51) 1.00 2.00 (0.61–6.59) 1.00 0.92 (0.40–2.09) 1.00 0.97 (0.60–1.55) 1.00 1.73 (0.52–5.70) 1.00 1.07 (0.52–2.20) 1.00 1.03 (0.67–1.59) 1.00
Adjusted for maternal age, ethnicity, parity, and socio-economic status (N ¼ 139967). Adjusted for maternal age, ethnicity, parity, socio-economic status and smoking during pregnancy (N ¼ 46946).
a
b
Bioelectromagnetics
Bioelectromagnetics
Male
44 181 530
Female
All
Male
Female
135 (297:28) 66 (146:14) 10 (57:38)
Female
All
Male
Difference in z-score (95% CI)b Female
19 73 192
19 73 192
Male Female
N (exposed)
15 59 187
Male Female
N ¼ 139967. Adjusted for maternal age, ethnicity, parity and socio-economic status. c Adjusted for maternal age, ethnicity, parity, socio-economic status and maternal smoking during pregnancy. d N ¼ 135506 due to missing information on gestational age.
b
a
20 77 199
Male
Female
251 (487:15) 99 (220:21) 13 (89:61)
Female
All
Male
Difference in z-score (95% CI)c Female
0.10 (0.73:0.54) 0.15 (1.13:0.82) 0.06 (0.90:0.77) 0.02 (0.35:0.30) 0.09 (0.57:0.39) 0.01 (0.41:0.44) 0.02 (0.17:0.21) 0.07 (0.355:0.20) 0.10 (0.17:0.36)
All
137 (421:146) 87 (56:230) 23 (57:103)
Male
Difference in weeks (95% CI)c
212 (395:29) 26 (119:67) 2 (54:57)
All
Difference in g (95% CI)c
0.35 (0.67:0.02) 0.23 (0.73:0.26) 0.44 (0.87:0.01) 0.03 (0.20:0.13) 0.17 (0.08:0.41) 0.19 (0.41:0.03) 0.00 (0.09:0.10) 0.06 (0.08:0.20) 0.05 (0.18:0.09)
Female
N (exposed)
15 60 191
Male
N (exposed)
Sex and gestational age-adjusted z-scores 50 m 43 41 0.19 (0.40:0.02) 0.21 (0.51:0.09) 0.18 (0.47:0.12) 15 100 m 176 172 0.01 (0.12:0.09) 0.09 (0.06:0.23) 0.12 (0.26:0.02) 59 200 m 547 513 0.02 (0.04:0.08) 0.04 (0.05:0.12) 0.00 (0.08:0.09) 187
Distance (m) Male Female
N (exposed)d
Gestational age (weeks) 50 m 43 41 100 m 176 172 200 m 548 513
Distance (m) Male
113 (283:56) 48 (37:133) 19 (29:67)
Male
Difference in weeks (95% CI)b
125 (243:7) 10 (69:49) 5 (29:39)
All
Difference in g (95% CI)b
0.05 (0.46:0.37) 0.05 (0.54:0.64) 0.14 (0.73:0.44) 0.01 (0.19:0.22) 0.02 (0.27:0.31) 0.01 (0.28:0.29) 0.00 (0.12:0.12) 0.02 (0.15:0.18) 0.02 (0.19:0.14)
Female
N (exposed)d
Birthweight (grams) 50 m 45 100 m 183 200 m 564
Distance (m)
N (exposed)a
TABLE 4. Association Between Average Birthweight and Gestational Age and Proximity to Nearest Residential ELF-EMF Source
206 Vocht et al.
EMF and Birth Outcomes
clinically significant outcomes. No differences in sex ratios relative to proximity to ELF-EMF sources were observed. These results are in line with previous studies [Robert et al., 1996; Auger et al., 2011] in that they do not show any statistically significant increased risks of PTB, LBW, SGA or on sex ratio. However, our results showed higher OR point estimates than the previous studies, especially after adjusting for maternal smoking which was not possible in the previous studies. In addition, our finding of a reduction of about 10% in birth weight amongst female newborns has not been reported previously. The main advantage of our study, which may offer an explanation for the differences in observed effect sizes between this and previous studies, is that we were able to, at least for part of the population, also adjust for maternal smoking during pregnancy; this is known to have an effect on birth outcomes [Bernstein et al., 2005] and indeed had a profound effect on observed risks in this study. In addition, we evaluated risks in relation to closest proximity to high voltage cables, overhead power lines, substations or towers and not, like previous studies, to overhead power lines only, which may have reduced misclassification of exposure. Our study also benefitted from a large population sample size, which was much larger than previously conducted studies, except for the study by Auger et al. [2011]. Nonetheless, even with the large population size in our study, and similar to what was reported for the population in the study by Auger et al. [2011] (N 700000), the prevalence of those living less than 50 m from an ELF-EMF sources was small (0.1% and 1.5%, respectively). There are a number of limitations of our study that may, to some extent, be alternative explanations for the observed associations. An important limitation of this study was that we were not able to measure EMF exposure of the study subjects directly, or that we were able to model residential exposure for all addresses such as was for example done in a recent study on adult cancers and power lines [Elliott et al., 2013]. We used proximity to underground cables, overhead lines, substations or towers as a proxy for true exposure to ELF-EMF. Although associated with significant exposure misclassification [Maslanyj et al., 2009], proximity to overhead lines alone can be a reasonably good [Turgeon et al., 2004] proxy of true residential exposure especially for nighttime exposure [Vistnes et al., 1997] which is generally a significant part of the 24 h day. Although conducted in Norway, measurement data indicated that exposure at home for those living close to power lines is truly different from that of those living farther out, and may
207
overshadow exposure from local sources in the home [Vistnes et al., 1997]. Modelled data for the UK also shows that mean magnetic field estimates exceeded background levels within 50 m of power lines and could be as high as 8592 nT [Elliott et al., 2013]. Also, data on indoor sources of EMF such as for example alarm clocks, televisions and home appliances were not available. This will inevitably have introduced measurement error but the magnitude of this is unknown. Regardless, it is unlikely that exposure measurement error is an alternative explanation for the increased risks observed in this study since there is no reason to assume differences between cases and noncases in their exposure. This is generally assumed to result in bias towards the Null [Maslanyj et al., 2009] although in theory they could have resulted in misclassification that is differential due to the dichotomization of the exposure [Gustafson, 2004]. Furthermore, possible residual confounding from social economic status (SES)/deprivation (IMD) adjustment may be an alternative explanation for the observed associations. We use IMD scores as a marker of SES which takes into account 7 key domains of deprivation, but is calculated at a neighborhood level and not at an individual level. Properties in very close proximity to overhead power lines or towers may be less desirable and difficult to sell and may therefore house people with lower socioeconomic status, which in turn is linked to certain lifestyle factors (including smoking). Analysis in property transaction data from Scotland for example, indicated that average house prices were 6–18% lower for similar properties sited within 100 m of a tower [Sims and Dent, 2005]. However, for this population the results in Table 2 indicate that the subjects living closest (50 m) to a residential EMF source were more often from the higher IMD quintiles than subjects living further away. Nonetheless, although it seems unlikely that this could have alternatively explained the results, residual confounding from socio-economic factors cannot be excluded. Another important limitation of this study was that the residential address of 28% of the initial 265974 women was missing, although exploratory analyses do not indicate any differential missing address data (Supplementary Online Material). Similarly, maternal smoking information was only available for 34% of women. However, for maternal smoking, it is possible that the women with missing smoking data may have had a higher percentage of smokers, and although there is no reason to assume that this missing data is directly related to proximity to residential ELF-EMF sources, the known correlation between smoking and socioeconomic status may indirectly have resulted in a Bioelectromagnetics
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Vocht et al.
correlation between smoking status (and thus potentially in missing data) and proximity to EMF sources. If this bias is present however, its magnitude is unknown. Similarly, statistical models were not adjusted for mother’s BMI because of the high percentage of missing data (33%) combined with the results of the univariate analyses (Online Supplementary Material), which indicated that only a BMI < 20 was associated with increased risk of adverse birth outcome of which only seven lived in close proximity to a source. As such, adjustment in multiple regression models was not meaningful, but nonetheless this may have resulted in residual confounding. Furthermore, the residential location for each woman that was linked to ELF-EMF sources was based on their residence at the time of birth. It would have been an improvement to have had residential history during the full pregnancy period, since specifically for pregnant women in Northwest England, a 9% mobility rate has been reported [Hodgson et al., 2009], but these data were not available. The causal agents for these findings may alternatively be other environmental exposures that are correlated with distance to EMF sources. In that respect, we further adjusted models for distance to a road (since traffic related air pollution) has also been associated to adverse birth outcomes [Silbergeld and Patrick, 2005], but this did not alter our findings (data not shown). Other reported exposures include lead, methyl mercury, PCBs, dioxins and related polyhalogenated aromatic hydrocarbons, chlorination disinfection by-products, solvents, environmental tobacco smoke, aeroallergens, sunlight and radiofrequency (RF) radiation, while residential proximity to hazardous waste disposal sites has also been reported [Wigle et al., 2007]. Another alternative explanation may be that adverse birth outcomes have been associated to reduced sperm quality resulting from parental (occupational) EMF exposure [Mjoen et al., 2006]. Again, it seems unlikely that any of these are as highly correlated with proximity to EMF sources as to explain the results from this study. In light of the above, if the results in this study are correct in that exposure to EMF from residential sources is causally related to a reduction in average birth weight in female babies and increased risk of adverse birth outcomes, then the biological mechanisms are unclear. At a cellular level, EMF have been reported to be able to influence the oxidative state and intracellular Ca2þsignalling patterns, which could go in either direction [Bauréus Koch et al., 2003] and which depends on the stage of cell differentiation [Morabito et al., 2010]. Disruption in the balance between plasma and vascular cell Ca2þ has in turn Bioelectromagnetics
(through nitric oxide synthase and nitric oxide) been linked to placental vascular function changes and subsequently adverse birth outcomes [Adamova et al., 2009]. The observed statistically significant reduction in birth weight of on average 212 g (after adjustment for smoking) was stronger in female newborns than in male newborns, with the latter not reaching statistical significance. This 10% reduction in birth weight, if corroborated and solely attributed to EMF exposure and not affected by residual confounding, could be clinically relevant and the effect size would be comparable to reported maternal smoking effects of up to nine cigarettes per day [Bernstein et al., 2005]. However, because very few pregnant women live close to studied sources of ELF-EMF, our findings, even if causal, would have negligible implications for measures that can be taken to reduce risk of suboptimal fetal growth in England. CONCLUSIONS In conclusion, this study amongst over 100000 singleton newborns indicates an association between living in close proximity (within 50 m) to residential ELF-EMF sources and suboptimal growth of the fetus, especially in female newborns. There were some indications this may result in clinically relevant effects, but no statistically significant effects were found. Chance or residual confounding however cannot be completely excluded; moreover the impact on public health is likely to be limited. REFERENCES Adamova Z, Ozkan S, Khalil RA. 2009. Vascular and cellular calcium in normal and hypertensive pregnancy. Curr Clin Pharmacol 4:172–190. Auger N, Joseph D, Goneau M, Daniel M. 2011. The relationship between residential proximity to extremely low frequency power transmission lines and adverse birth outcomes. J Epidemiol Community Health 65:83–85. Auger N, Park AL, Yacouba S, Goneau M, Zayed J. 2012. Stillbirth and residential proximity to extremely low frequency power transmission lines: A retrospective cohort study. Occup Environ Med 69:147–149. Barker D. 2006. Adult consequences of fetal growth restriction. Clin Obstet Gynecol 49:270–283. Bauréus Koch CLM, Sommarin M, Persson BBR, Salford LG, Eberhardt JL. 2003. Interaction between weak low frequency magnetic fields and cell membranes. Bioelectromagnetics 24:395–402. Bernstein IM, Mongeon JA, Badger GJ, Solomon L, Heil SH, Higgins ST. 2005. Maternal smoking and its association with birth weight. Obstet Gynecol 106:986–991. Boo H, Harding J. 2006. The developmental origins of adult disease (Barker) hypothesis. Aust Nz J Obstet Gyn 46:4–14.
EMF and Birth Outcomes British National Grid. 2013. Transmission network: Shape files. Available from: http://www2.nationalgrid.com/uk/services/ land-and-development/planning-authority/shape-files/ (Last accessed 5 November 2013). de Vocht F. 2013. Adult cancers near high-voltage power lines. Epidemiology 24:782. Directgov 2007. Communities and neighbourhoods: Indices of deprivation 2007. Communities and local government. Available from: http://webarchive.nationalarchives.gov.uk/þ/http:/ www.communities.gov.uk/communities/neighbourhoodrenewal/ deprivation/deprivation07/ (Last accessed 1 March 2013). Elliott P, Shaddick G, Douglass M, de Hoogh K, Briggs DJ, Toledano MB. 2013. Adult cancers near high-voltage overhead power lines. Epidemiology 24:184–190. Feychting M. 2005. Non-cancer EMF effects related to children. Bioelectromagnetics 23(Suppl 7):S69–S74. Grigoriev YG, Grigoriev OA, Ivanov AA, Lyaginskaya AM, Merkulov AV, Shagina NB, Maltsev VN, Lévêque P, Ulanova AM, Osipov VA, Shafirkin AV. 2010. Confirmation studies of Soviet research on immunological effects of microwaves: Russian immunology results. Bioelectromagnetics 31:589–602. Gustafson P. 2004. Dichotomization of mismeasured continuous variables. In: Gustafson P, editor. Measurement error and misclassification in statistics and epidemiology. Impacts and Bayesian adjustments. Boca Raton (FL): Chapman & Hall/ CRC. p 143. Hodgson S, Shirley M, Bythell M, Rankin J. 2009. Residential mobility during pregnancy in the north of England. BMC Pregnancy Childbirth 9:52. Irgens A, Kruger K, Skorve AH, Irgens LM. 1997. Male proportion in offspring of parents exposed to strong static and extremely low-frequency electromagnetic fields in Norway. Am J Ind Med 32:557–561. Khashan A, Baker P, Kenny L. 2010. Preterm birth and reduced birthweight in first and second teenage pregnancies: A register-based cohort study. BMC Pregnancy Childbirth 9:36. Kroll ME, Swanson J, Vincent TJ, Draper GJ. 2010. Childhood cancer and magnetic fields from high-voltage power lines in England and Wales: A case-control study. Br J Cancer 103:1122–1127. Li DK, Neutra RR. 2002. Magnetic fields and miscarriage. Epidemiology 13:237–238. Li D, Chen H, Odouli R. 2011. Maternal exposure to magnetic fields during pregnancy in relation to the risk of asthma in offspring. Arch Pediatr Adolesc Med 165:945–950. Maslanyj M, Simpson J, Roman E, Schüz J. 2009. Power frequency magnetic fields and risk of childhood leukaemia: Misclassification of exposure from the use of the ’distance from power line’ exposure surrogate. Bioelectromagnetics 30:183–188. Mjoen G, Saetre DO, Lie RT, Tynes T, Blaasaas KG, Hannevik M, Irgens LM. 2006. Paternal occupational exposure to radiofrequency electromagnetic fields and risk of adverse pregnancy outcome. Eur J Epidemiol 21:529–535. Morabito C, Rovetta F, Bizzarri M, Mazzoleni G, Fano G, Mariggio MA. 2010. Modulation of redox status and
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calcium handling by extremely low frequency electromagnetic fields in C2C12 muscle cells: A real-time, single-cell approach. Free Radic Biol Med 48:579–589. Philips A, O’Carroll M, Henshaw D, Lamburn G. 2013. Adult cancers near high-voltage power lines. Epidemiology 24: 782–783. Robert E, Harris JA, Robert O, Selvin S. 1996. Case-control study on maternal residential proximity to high voltage power lines and congenital anomalies in France. Paediatr Perinat Epidemiol 10:32–38. Saunders RD, McCaig CD. 2005. Developmental effects of physiologically weak electric fields and heat: An overview. Bioelectromagnetics 26(Suppl 7):S127–S132. Savitz DA, Ananth CV. 1994. Residential magnetic fields, wire codes, and pregnancy outcome. Bioelectromagnetics 15: 271–273. Silbergeld EK, Patrick TE. 2005. Environmental exposures, toxicologic mechanisms, and adverse pregnancy outcomes. Am J Obstet Gynecol 192:S11–S21. Sims S, Dent P. 2005. High-voltage overhead power lines and property values: A residential study in the UK. Urban Studies 42:665–694. Swanson J, Kheifets L. 2006. Biophysical mechanisms: A component in the weight of evidence for health effects of power-frequency electric and magnetic fields. Radiat Res 165:470–478. Turgeon A, Bourdages M, Levallois P, Gauvin D, Gingras S, Deadman JE, Goulet DL, Plante M. 2004. Experimental validation of a statistical model for evaluating the past or future magnetic field exposures of a population living near power lines. Bioelectromagnetics 25:374–379. UK Data Service. 2010. GeoConvert. Available from: http:// geoconvert.mimas.ac.uk/ (Last accessed 25 September 2013). United Nations Children’s Fund (UNICEF). and World Health Organization (WHO). 2004. Low birthweight: Country, regional and global estimates. Available from: http://www. unicef.org/publications/index_24840.html (Last accessed 11 September 2013). Vistnes AI, Ramberg GB, Bjørnevik LR, Tynes T, Haldorsen T. 1997. Exposure of children to residential magnetic fields in Norway: Is proximity to power lines an adequate predictor of exposure? Bioelectromagnetics 18:47–57. WHO. 2012. Born too soon: The global action report on preterm birth. Geneva: World Health Organization. Wigle DT, Arbuckle TE, Walker M, Wade MG, Liu S, Krewski D. 2007. Environmental hazards: Evidence for effects on child health. J Toxicol Environ Health B Crit Rev 10:3–39.
SUPPORTING INFORMATION Additional Supporting information may be found in the online version of this article at the publisher’s web-site.
Bioelectromagnetics
Maternal Residential Proximity to Sources of Extremely Low Frequency Electromagnetic Fields and Adverse Birth Outcomes in a UKCohort Frank deVocht,1* Kimberly Hannam,2 Philip Baker,3 and Raymond Agius1 1
Centre for Occupational and Environmental Health, Institute of Population Health, ManchesterAcademic Health Sciences Centre,The University of Manchester, Manchester, United Kingdom 2 Maternal and Fetal Health Research, Medical and Human Sciences, Manchester Academic Health Sciences Centre,The University of Manchester, Manchester, United Kingdom 3 Liggins Institute, University of Auckland, Auckland, New Zealand
Studies have suggested that exposure to extremely low frequency electromagnetic fields (ELF-EMF) may be associated with increased risk of adverse birth outcomes. This study tested the hypothesis that close proximity to residential ELF-EMF sources is associated with a reduction in birth weight and increased the risk of low birthweight (LBW), small for gestational age (SGA) and spontaneous preterm birth (SPTB). Closest residential proximity to high voltage cables, overhead power lines, substations or towers during pregnancy was calculated for 140356 singleton live births between 2004 and 2008 in Northwest England. Associations between proximity and risk for LBW, SGA and SPTB were calculated, as well as associations with birth weight directly. Associations were adjusted for maternal age, ethnicity, parity and for part of the population additionally for maternal smoking during pregnancy. Reduced average birth weight of 212 g (95% confidence interval (CI): 395 to 29 g) was found for close proximity to a source, and was largest for female births (251 g (95% CI: 487 to 15 g)). No statistically significant increased risks for any clinical birth outcomes with residential proximity of 50 m or less were observed. Living close (50 m or less) to a residential ELF-EMF source during pregnancy is associated with suboptimal growth in utero, with stronger effects in female than in males. However, only a few pregnant women live this close to high voltage cables, overhead power lines, substations or towers, likely limiting its public health impact. Bioelectromagnetics 35:201–209, 2014. © 2014 Wiley Periodicals, Inc. Key words: power lines; perinatal outcomes; birth outcomes; cohort study; birth weight
INTRODUCTION Suboptimal growth in utero has been linked to an increased risk of long-term health problems, including type 2 diabetes and cardiovascular disease (Barker hypothesis) [Barker, 2006]. It is unclear why, despite better provision of antenatal services in developed countries, rates of preterm birth (PTB) (birth prior to 27 weeks of completed gestation) are increasing [World Health Organisation (WHO), 2012] and rates of low birthweight (LBW) (100 200 >200 50 >50 100 >100 200 >200
6/83 9288/130590 21/341 9273/130332 60/1031 9234/129642 7/79 12201/126693 21/337 12187/126435 74/1010 12134/125762 7/77 7858/127867 18/331 7847/127613 65/996 7800/126948 7/77 8631/127094 21/328 8617/126843 73/988 8565/126183
1.37 (0.59–3.14) 1.00 1.09 (0.71–1.71) 1.00 1.00 (0.77–1.30) 1.00 1.27 (0.58–2.76) 1.00 0.85 (0.55–1.33) 1.00 0.97 (0.77–1.24) 1.00 1.73 (0.79–3.77) 1.00 1.00 (0.62–1.60) 1.00 1.16 (0.91–1.50) 1.00 1.56 (0.72–3.39) 1.00 1.06 (0.68–1.63) 1.00 1.18 (0.93–1.51) 1.00
4/31 2904/44007 9/127 2899/43911 20/369 2888/43669 5/29 3995/42613 10/124 3990/42518 27/359 3973/42283 3/31 2415/43459 6/127 2412/43363 18/362 2400/43128 3/31 2732/43142 8/125 125/43048 22/358 2713/42815
2.52 (0.88–7.26) 1.00 1.28 (0.65–2.53) 1.00 0.99 (0.63–1.57) 1.00 2.51 (0.95–6.61) 1.00 1.08 (0.56–2.07) 1.00 1.01 (0.68–1.51) 1.00 2.00 (0.61–6.59) 1.00 0.92 (0.40–2.09) 1.00 0.97 (0.60–1.55) 1.00 1.73 (0.52–5.70) 1.00 1.07 (0.52–2.20) 1.00 1.03 (0.67–1.59) 1.00
Adjusted for maternal age, ethnicity, parity, and socio-economic status (N ¼ 139967). Adjusted for maternal age, ethnicity, parity, socio-economic status and smoking during pregnancy (N ¼ 46946).
a
b
Bioelectromagnetics
Bioelectromagnetics
Male
44 181 530
Female
All
Male
Female
135 (297:28) 66 (146:14) 10 (57:38)
Female
All
Male
Difference in z-score (95% CI)b Female
19 73 192
19 73 192
Male Female
N (exposed)
15 59 187
Male Female
N ¼ 139967. Adjusted for maternal age, ethnicity, parity and socio-economic status. c Adjusted for maternal age, ethnicity, parity, socio-economic status and maternal smoking during pregnancy. d N ¼ 135506 due to missing information on gestational age.
b
a
20 77 199
Male
Female
251 (487:15) 99 (220:21) 13 (89:61)
Female
All
Male
Difference in z-score (95% CI)c Female
0.10 (0.73:0.54) 0.15 (1.13:0.82) 0.06 (0.90:0.77) 0.02 (0.35:0.30) 0.09 (0.57:0.39) 0.01 (0.41:0.44) 0.02 (0.17:0.21) 0.07 (0.355:0.20) 0.10 (0.17:0.36)
All
137 (421:146) 87 (56:230) 23 (57:103)
Male
Difference in weeks (95% CI)c
212 (395:29) 26 (119:67) 2 (54:57)
All
Difference in g (95% CI)c
0.35 (0.67:0.02) 0.23 (0.73:0.26) 0.44 (0.87:0.01) 0.03 (0.20:0.13) 0.17 (0.08:0.41) 0.19 (0.41:0.03) 0.00 (0.09:0.10) 0.06 (0.08:0.20) 0.05 (0.18:0.09)
Female
N (exposed)
15 60 191
Male
N (exposed)
Sex and gestational age-adjusted z-scores 50 m 43 41 0.19 (0.40:0.02) 0.21 (0.51:0.09) 0.18 (0.47:0.12) 15 100 m 176 172 0.01 (0.12:0.09) 0.09 (0.06:0.23) 0.12 (0.26:0.02) 59 200 m 547 513 0.02 (0.04:0.08) 0.04 (0.05:0.12) 0.00 (0.08:0.09) 187
Distance (m) Male Female
N (exposed)d
Gestational age (weeks) 50 m 43 41 100 m 176 172 200 m 548 513
Distance (m) Male
113 (283:56) 48 (37:133) 19 (29:67)
Male
Difference in weeks (95% CI)b
125 (243:7) 10 (69:49) 5 (29:39)
All
Difference in g (95% CI)b
0.05 (0.46:0.37) 0.05 (0.54:0.64) 0.14 (0.73:0.44) 0.01 (0.19:0.22) 0.02 (0.27:0.31) 0.01 (0.28:0.29) 0.00 (0.12:0.12) 0.02 (0.15:0.18) 0.02 (0.19:0.14)
Female
N (exposed)d
Birthweight (grams) 50 m 45 100 m 183 200 m 564
Distance (m)
N (exposed)a
TABLE 4. Association Between Average Birthweight and Gestational Age and Proximity to Nearest Residential ELF-EMF Source
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clinically significant outcomes. No differences in sex ratios relative to proximity to ELF-EMF sources were observed. These results are in line with previous studies [Robert et al., 1996; Auger et al., 2011] in that they do not show any statistically significant increased risks of PTB, LBW, SGA or on sex ratio. However, our results showed higher OR point estimates than the previous studies, especially after adjusting for maternal smoking which was not possible in the previous studies. In addition, our finding of a reduction of about 10% in birth weight amongst female newborns has not been reported previously. The main advantage of our study, which may offer an explanation for the differences in observed effect sizes between this and previous studies, is that we were able to, at least for part of the population, also adjust for maternal smoking during pregnancy; this is known to have an effect on birth outcomes [Bernstein et al., 2005] and indeed had a profound effect on observed risks in this study. In addition, we evaluated risks in relation to closest proximity to high voltage cables, overhead power lines, substations or towers and not, like previous studies, to overhead power lines only, which may have reduced misclassification of exposure. Our study also benefitted from a large population sample size, which was much larger than previously conducted studies, except for the study by Auger et al. [2011]. Nonetheless, even with the large population size in our study, and similar to what was reported for the population in the study by Auger et al. [2011] (N 700000), the prevalence of those living less than 50 m from an ELF-EMF sources was small (0.1% and 1.5%, respectively). There are a number of limitations of our study that may, to some extent, be alternative explanations for the observed associations. An important limitation of this study was that we were not able to measure EMF exposure of the study subjects directly, or that we were able to model residential exposure for all addresses such as was for example done in a recent study on adult cancers and power lines [Elliott et al., 2013]. We used proximity to underground cables, overhead lines, substations or towers as a proxy for true exposure to ELF-EMF. Although associated with significant exposure misclassification [Maslanyj et al., 2009], proximity to overhead lines alone can be a reasonably good [Turgeon et al., 2004] proxy of true residential exposure especially for nighttime exposure [Vistnes et al., 1997] which is generally a significant part of the 24 h day. Although conducted in Norway, measurement data indicated that exposure at home for those living close to power lines is truly different from that of those living farther out, and may
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overshadow exposure from local sources in the home [Vistnes et al., 1997]. Modelled data for the UK also shows that mean magnetic field estimates exceeded background levels within 50 m of power lines and could be as high as 8592 nT [Elliott et al., 2013]. Also, data on indoor sources of EMF such as for example alarm clocks, televisions and home appliances were not available. This will inevitably have introduced measurement error but the magnitude of this is unknown. Regardless, it is unlikely that exposure measurement error is an alternative explanation for the increased risks observed in this study since there is no reason to assume differences between cases and noncases in their exposure. This is generally assumed to result in bias towards the Null [Maslanyj et al., 2009] although in theory they could have resulted in misclassification that is differential due to the dichotomization of the exposure [Gustafson, 2004]. Furthermore, possible residual confounding from social economic status (SES)/deprivation (IMD) adjustment may be an alternative explanation for the observed associations. We use IMD scores as a marker of SES which takes into account 7 key domains of deprivation, but is calculated at a neighborhood level and not at an individual level. Properties in very close proximity to overhead power lines or towers may be less desirable and difficult to sell and may therefore house people with lower socioeconomic status, which in turn is linked to certain lifestyle factors (including smoking). Analysis in property transaction data from Scotland for example, indicated that average house prices were 6–18% lower for similar properties sited within 100 m of a tower [Sims and Dent, 2005]. However, for this population the results in Table 2 indicate that the subjects living closest (50 m) to a residential EMF source were more often from the higher IMD quintiles than subjects living further away. Nonetheless, although it seems unlikely that this could have alternatively explained the results, residual confounding from socio-economic factors cannot be excluded. Another important limitation of this study was that the residential address of 28% of the initial 265974 women was missing, although exploratory analyses do not indicate any differential missing address data (Supplementary Online Material). Similarly, maternal smoking information was only available for 34% of women. However, for maternal smoking, it is possible that the women with missing smoking data may have had a higher percentage of smokers, and although there is no reason to assume that this missing data is directly related to proximity to residential ELF-EMF sources, the known correlation between smoking and socioeconomic status may indirectly have resulted in a Bioelectromagnetics
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correlation between smoking status (and thus potentially in missing data) and proximity to EMF sources. If this bias is present however, its magnitude is unknown. Similarly, statistical models were not adjusted for mother’s BMI because of the high percentage of missing data (33%) combined with the results of the univariate analyses (Online Supplementary Material), which indicated that only a BMI < 20 was associated with increased risk of adverse birth outcome of which only seven lived in close proximity to a source. As such, adjustment in multiple regression models was not meaningful, but nonetheless this may have resulted in residual confounding. Furthermore, the residential location for each woman that was linked to ELF-EMF sources was based on their residence at the time of birth. It would have been an improvement to have had residential history during the full pregnancy period, since specifically for pregnant women in Northwest England, a 9% mobility rate has been reported [Hodgson et al., 2009], but these data were not available. The causal agents for these findings may alternatively be other environmental exposures that are correlated with distance to EMF sources. In that respect, we further adjusted models for distance to a road (since traffic related air pollution) has also been associated to adverse birth outcomes [Silbergeld and Patrick, 2005], but this did not alter our findings (data not shown). Other reported exposures include lead, methyl mercury, PCBs, dioxins and related polyhalogenated aromatic hydrocarbons, chlorination disinfection by-products, solvents, environmental tobacco smoke, aeroallergens, sunlight and radiofrequency (RF) radiation, while residential proximity to hazardous waste disposal sites has also been reported [Wigle et al., 2007]. Another alternative explanation may be that adverse birth outcomes have been associated to reduced sperm quality resulting from parental (occupational) EMF exposure [Mjoen et al., 2006]. Again, it seems unlikely that any of these are as highly correlated with proximity to EMF sources as to explain the results from this study. In light of the above, if the results in this study are correct in that exposure to EMF from residential sources is causally related to a reduction in average birth weight in female babies and increased risk of adverse birth outcomes, then the biological mechanisms are unclear. At a cellular level, EMF have been reported to be able to influence the oxidative state and intracellular Ca2þsignalling patterns, which could go in either direction [Bauréus Koch et al., 2003] and which depends on the stage of cell differentiation [Morabito et al., 2010]. Disruption in the balance between plasma and vascular cell Ca2þ has in turn Bioelectromagnetics
(through nitric oxide synthase and nitric oxide) been linked to placental vascular function changes and subsequently adverse birth outcomes [Adamova et al., 2009]. The observed statistically significant reduction in birth weight of on average 212 g (after adjustment for smoking) was stronger in female newborns than in male newborns, with the latter not reaching statistical significance. This 10% reduction in birth weight, if corroborated and solely attributed to EMF exposure and not affected by residual confounding, could be clinically relevant and the effect size would be comparable to reported maternal smoking effects of up to nine cigarettes per day [Bernstein et al., 2005]. However, because very few pregnant women live close to studied sources of ELF-EMF, our findings, even if causal, would have negligible implications for measures that can be taken to reduce risk of suboptimal fetal growth in England. CONCLUSIONS In conclusion, this study amongst over 100000 singleton newborns indicates an association between living in close proximity (within 50 m) to residential ELF-EMF sources and suboptimal growth of the fetus, especially in female newborns. There were some indications this may result in clinically relevant effects, but no statistically significant effects were found. Chance or residual confounding however cannot be completely excluded; moreover the impact on public health is likely to be limited. REFERENCES Adamova Z, Ozkan S, Khalil RA. 2009. Vascular and cellular calcium in normal and hypertensive pregnancy. Curr Clin Pharmacol 4:172–190. Auger N, Joseph D, Goneau M, Daniel M. 2011. The relationship between residential proximity to extremely low frequency power transmission lines and adverse birth outcomes. J Epidemiol Community Health 65:83–85. Auger N, Park AL, Yacouba S, Goneau M, Zayed J. 2012. Stillbirth and residential proximity to extremely low frequency power transmission lines: A retrospective cohort study. Occup Environ Med 69:147–149. Barker D. 2006. Adult consequences of fetal growth restriction. Clin Obstet Gynecol 49:270–283. Bauréus Koch CLM, Sommarin M, Persson BBR, Salford LG, Eberhardt JL. 2003. Interaction between weak low frequency magnetic fields and cell membranes. Bioelectromagnetics 24:395–402. Bernstein IM, Mongeon JA, Badger GJ, Solomon L, Heil SH, Higgins ST. 2005. Maternal smoking and its association with birth weight. Obstet Gynecol 106:986–991. Boo H, Harding J. 2006. The developmental origins of adult disease (Barker) hypothesis. Aust Nz J Obstet Gyn 46:4–14.
EMF and Birth Outcomes British National Grid. 2013. Transmission network: Shape files. Available from: http://www2.nationalgrid.com/uk/services/ land-and-development/planning-authority/shape-files/ (Last accessed 5 November 2013). de Vocht F. 2013. Adult cancers near high-voltage power lines. Epidemiology 24:782. Directgov 2007. Communities and neighbourhoods: Indices of deprivation 2007. Communities and local government. Available from: http://webarchive.nationalarchives.gov.uk/þ/http:/ www.communities.gov.uk/communities/neighbourhoodrenewal/ deprivation/deprivation07/ (Last accessed 1 March 2013). Elliott P, Shaddick G, Douglass M, de Hoogh K, Briggs DJ, Toledano MB. 2013. Adult cancers near high-voltage overhead power lines. Epidemiology 24:184–190. Feychting M. 2005. Non-cancer EMF effects related to children. Bioelectromagnetics 23(Suppl 7):S69–S74. Grigoriev YG, Grigoriev OA, Ivanov AA, Lyaginskaya AM, Merkulov AV, Shagina NB, Maltsev VN, Lévêque P, Ulanova AM, Osipov VA, Shafirkin AV. 2010. Confirmation studies of Soviet research on immunological effects of microwaves: Russian immunology results. Bioelectromagnetics 31:589–602. Gustafson P. 2004. Dichotomization of mismeasured continuous variables. In: Gustafson P, editor. Measurement error and misclassification in statistics and epidemiology. Impacts and Bayesian adjustments. Boca Raton (FL): Chapman & Hall/ CRC. p 143. Hodgson S, Shirley M, Bythell M, Rankin J. 2009. Residential mobility during pregnancy in the north of England. BMC Pregnancy Childbirth 9:52. Irgens A, Kruger K, Skorve AH, Irgens LM. 1997. Male proportion in offspring of parents exposed to strong static and extremely low-frequency electromagnetic fields in Norway. Am J Ind Med 32:557–561. Khashan A, Baker P, Kenny L. 2010. Preterm birth and reduced birthweight in first and second teenage pregnancies: A register-based cohort study. BMC Pregnancy Childbirth 9:36. Kroll ME, Swanson J, Vincent TJ, Draper GJ. 2010. Childhood cancer and magnetic fields from high-voltage power lines in England and Wales: A case-control study. Br J Cancer 103:1122–1127. Li DK, Neutra RR. 2002. Magnetic fields and miscarriage. Epidemiology 13:237–238. Li D, Chen H, Odouli R. 2011. Maternal exposure to magnetic fields during pregnancy in relation to the risk of asthma in offspring. Arch Pediatr Adolesc Med 165:945–950. Maslanyj M, Simpson J, Roman E, Schüz J. 2009. Power frequency magnetic fields and risk of childhood leukaemia: Misclassification of exposure from the use of the ’distance from power line’ exposure surrogate. Bioelectromagnetics 30:183–188. Mjoen G, Saetre DO, Lie RT, Tynes T, Blaasaas KG, Hannevik M, Irgens LM. 2006. Paternal occupational exposure to radiofrequency electromagnetic fields and risk of adverse pregnancy outcome. Eur J Epidemiol 21:529–535. Morabito C, Rovetta F, Bizzarri M, Mazzoleni G, Fano G, Mariggio MA. 2010. Modulation of redox status and
209
calcium handling by extremely low frequency electromagnetic fields in C2C12 muscle cells: A real-time, single-cell approach. Free Radic Biol Med 48:579–589. Philips A, O’Carroll M, Henshaw D, Lamburn G. 2013. Adult cancers near high-voltage power lines. Epidemiology 24: 782–783. Robert E, Harris JA, Robert O, Selvin S. 1996. Case-control study on maternal residential proximity to high voltage power lines and congenital anomalies in France. Paediatr Perinat Epidemiol 10:32–38. Saunders RD, McCaig CD. 2005. Developmental effects of physiologically weak electric fields and heat: An overview. Bioelectromagnetics 26(Suppl 7):S127–S132. Savitz DA, Ananth CV. 1994. Residential magnetic fields, wire codes, and pregnancy outcome. Bioelectromagnetics 15: 271–273. Silbergeld EK, Patrick TE. 2005. Environmental exposures, toxicologic mechanisms, and adverse pregnancy outcomes. Am J Obstet Gynecol 192:S11–S21. Sims S, Dent P. 2005. High-voltage overhead power lines and property values: A residential study in the UK. Urban Studies 42:665–694. Swanson J, Kheifets L. 2006. Biophysical mechanisms: A component in the weight of evidence for health effects of power-frequency electric and magnetic fields. Radiat Res 165:470–478. Turgeon A, Bourdages M, Levallois P, Gauvin D, Gingras S, Deadman JE, Goulet DL, Plante M. 2004. Experimental validation of a statistical model for evaluating the past or future magnetic field exposures of a population living near power lines. Bioelectromagnetics 25:374–379. UK Data Service. 2010. GeoConvert. Available from: http:// geoconvert.mimas.ac.uk/ (Last accessed 25 September 2013). United Nations Children’s Fund (UNICEF). and World Health Organization (WHO). 2004. Low birthweight: Country, regional and global estimates. Available from: http://www. unicef.org/publications/index_24840.html (Last accessed 11 September 2013). Vistnes AI, Ramberg GB, Bjørnevik LR, Tynes T, Haldorsen T. 1997. Exposure of children to residential magnetic fields in Norway: Is proximity to power lines an adequate predictor of exposure? Bioelectromagnetics 18:47–57. WHO. 2012. Born too soon: The global action report on preterm birth. Geneva: World Health Organization. Wigle DT, Arbuckle TE, Walker M, Wade MG, Liu S, Krewski D. 2007. Environmental hazards: Evidence for effects on child health. J Toxicol Environ Health B Crit Rev 10:3–39.
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