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in your review and critique of the research of other public health professionals, you may find evidence not only of selection bias but of information bias as well. Information bias results from errors made in the collection of information obtained in a study. For example, participants’ self-report of their diet may be inaccurate for many reasons. They may not remember what they ate, or they may want to portray themselves as making healthier choices than they typically make. Regardless of the reason, the information collected is not accurate and therefore introduces bias into the analysis.
Knowing how to analyze information biases in reported research or your own studies allows you to assess whether or not you believe study results are valid given the information collected. This can guide the application of evidence-based practice as well as your own research agenda.
For this Discussion, you will be assigned one a article (attached; please submit your discussion posts individually. As you review the article you have been assigned, consider elements in the study design that might indicate information bias. Think about how the researchers did or did not guard against information biases in their study design.
Post an explanation of possible information bias in the study, including the effect that the measurement error may have had on study results and interpretation. Then explain whether or not information bias was effectively minimized in the study. Finally, provide one alternative method for minimizing information bias and explain how the method might minimize error.
Hodgson S, Lutz PW, Shirley MD, Bythell M, Rankin J. Exposure misclassification due to residential mobility during pregnancy. International Journal of Hygiene and Environmental Health 2015, 218(4), 414-421.
© 2015 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
DOI link to article:
This work is licensed under a Creative Commons Attribution 4.0 International License
S M a
A R R A
K E M R E E
u i i m s t c
International Journal of Hygiene and Environmental Health 218 (2015) 414–421
Contents lists available at ScienceDirect
International Journal of Hygiene and Environmental Health
jou rn al hom ep age: www.elsev ier .com/ locate / i jheh
xposure misclassification due to residential mobility uring pregnancy
usan Hodgsona,b,∗, Peter W.W. Lurzc,d, Mark D.F. Shirleyc, ary Bythell e, Judith Rankinb,e
MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Imperial College London, United Kingdom Institute of Health & Society, Newcastle University, Newcastle upon Tyne, United Kingdom School of Biology, Newcastle University, Newcastle upon Tyne, United Kingdom Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom Regional Maternity Survey Office, Newcastle upon Tyne, United Kingdom
r t i c l e i n f o
rticle history: eceived 26 September 2014 eceived in revised form 13 March 2015 ccepted 18 March 2015
eywords: nvironmental exposure aternal exposure
esidential mobility xposure assessment xposure error
a b s t r a c t
Objectives: Pregnant women are a highly mobile group, yet studies suggest exposure error due to migra- tion in pregnancy is minimal. We aimed to investigate the impact of maternal residential mobility on exposure to environmental variables (urban fabric, roads and air pollution (PM10 and NO2)) and socio-economic factors (deprivation) that varied spatially and temporally. Methods: We used data on residential histories for deliveries at ≥24 weeks gestation recorded by the Northern Congenital Abnormality Survey, 2000–2008 (n = 5399) to compare: (a) exposure at conception assigned to maternal postcode at delivery versus maternal postcode at conception, and (b) exposure at conception assigned to maternal postcode at delivery versus mean exposure based on residences throughout pregnancy. Results: In this population, 24.4% of women moved during pregnancy. Depending on the exposure vari-able assessed, 1–12% of women overall were assigned an exposure at delivery >1SD different to that at conception, and 2–25% assigned an exposure at delivery >1SD different to the mean exposure throughout pregnancy. Conclusions: To meaningfully explore the subtle associations between environmental exposures and health, consideration must be given to error introduced by residential mobility.
ors. P© 2015 The Auth
Epidemiological studies carried out at the ecological level, or sing routinely collected health data, often assign exposure to an
ndividual’s residence at a single time point, such as birth, hospital- sation or death. This approach fails to account for individuals who
ight have migrated into or out of the population or for periodic pells away from a residence where levels of exposure are likelyo be different from those experienced at home. Such migrations ould result in exposure error or misclassification, reduced study
∗ Corresponding author at: MRC-PHE Centre for Environment & Health, Depart- ent of Epidemiology & Biostatistics, Imperial College London, St. Mary’s Campus, orfolk Place, London W2 1PG, United Kingdom. Tel.: +44 020 7594 2789.
E-mail addresses: email@example.com (S. Hodgson), firstname.lastname@example.org (P.W.W. Lurz), email@example.com (M.D.F. Shirley),
firstname.lastname@example.org (M. Bythell), email@example.com (J. Rankin).
ttp://dx.doi.org/10.1016/j.ijheh.2015.03.007 438-4639/© 2015 The Authors. Published by Elsevier GmbH. This is an open access artic
ublished by Elsevier GmbH. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
power, and may result in biased risk estimates (Armstrong, 1998; Blair et al., 2007; Khoury et al., 1988).
Many environmental epidemiological studies of birth outcomes assign a measure of exposure based on maternal residential loca- tion at delivery because this information is readily available. The relatively short period between exposure and disease manifesta- tion should mean that studies on congenital anomalies are less prone to migration bias, as there is less time in which the pop- ulation can migrate. However, there is now a significant body of literature showing that pregnant women are a highly mobile group, with 10–30% of women moving residence during pregnancy (Bell and Belanger, 2012; Canfield et al., 2006; Fell et al., 2004; Hodgson et al., 2009; Khoury et al., 1988; Shaw and Malcoe, 1992; Zender et al., 2001).
Theoretical papers on the implications of residential mobilityduring pregnancy on the ability to detect environmental terato- gens (Khoury et al., 1988) and impacts of differential mobility (Schulman et al., 1993) remain relevant, and a study showing the impact of mobility on real-life exposure scenarios and on
le under the CC BY license (http://creativecommons.org/licenses/by/4.0/).dx.doi.org/10.1016/j.ijheh.2015.03.007http://www.sciencedirect.com/science/journal/14384639http://www.elsevier.com/locate/ijhehhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.ijheh.2015.03.007&domain=pdfhttp://creativecommons.org/licenses/by/4.0/mailto:firstname.lastname@example.org:email@example.com:firstname.lastname@example.org:email@example.com:firstname.lastname@example.org/10.1016/j.ijheh.2015.03.007http://creativecommons.org/licenses/by/4.0/
iene and Environmental Health 218 (2015) 414–421 415
e i o e i v u 2 a 2 i a p
p n n a o b a c g p g g c c a e s v s b f t t d g s w
a d t N u N A c ( w o t a p o l p e d e
Table 1 Social and environmental variables assigned to maternal residential postcodes to explore the impact of residential mobility during pregnancy on characterisation of exposure.
Socio-economic status 1. Index of Multiple Deprivation at Super Output Area level
Data source Office for National Statistics Variable type Continuous and quintile, area level Spatial resolution Super Output Area Temporal resolution n/a (data for 2007 used for whole study period)
2. Index of Multiple Deprivation at Local Authority level Data source Office for National Statistics Variable type Continuous and quintile, area level Spatial resolution Local Authority Temporal resolution n/a (data for 2007 used for whole study period)
Land cover 3. % Continuous Urban Fabric within 500 m buffer of postcode
Data source CORINE land cover 2000v8a
Variable type Continuous and dichotomous, individual level Spatial resolution 100 m Temporal resolution n/a (data from 2000 used for whole study period)
4. % Discontinuous Urban Fabric within 500 m buffer of postcode Data source CORINE land cover 2000v8a
Variable type Continuous and quintile, individual level Spatial resolution 100 m Temporal resolution n/a (data from 2000 used for whole study period)
Roads 5. Total length (m) of roads (motorways, A and B roads) within 500 m buffer of postcode
Data source Strategi 2011b
Variable type Continuous and quintile, individual level Spatial resolution 1 m Temporal resolution n/a (data from 2011 used for whole study period)
Air pollution 6. Annual background PM10
Data source DEFRA Ambient Air Quality Assessment (UKAAQA)c
Variable type Continuous and quintile, individual level Spatial resolution 1 km grid square Temporal resolution Annual mean, 2001–2008
7. Daily NO2 Data source DEFRA Automatic Urban and Rural Networkd
Variable type Continuous and quintile, individual level Spatial resolution Nearest monitor (for those living within 15 km of a
monitor) Temporal resolution Daily mean (averaged over first trimester),
a http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000- clc2000-100-m-version-8-2005.
b www.ordnancesurvey.co.uk/oswebsite/docs/user-guides/strategi-user- guide.pdf.
S. Hodgson et al. / International Journal of Hyg
nvironmental risk factors likely to confer small, but important ncreases in risk, is overdue. In this paper we investigate the impact f residential mobility during pregnancy on the measurement of xposure to a range of environmental factors previously explored n aetiological research (for example area-level measures of depri- ation (Dibben et al., 2006; Janevic et al., 2010), land cover (e.g. rban/rural classifications) (Hillemeier et al., 2007; Langlois et al., 010), road density/proximity to roads (Yorifuji et al., 2011) and ir pollutants (Dugandzic et al., 2006; Hansen et al., 2009; Xu et al., 011)), and quantify the exposure error likely to be introduced
nto a study reliant on maternal residential location at delivery s a proxy for residential location at conception and throughout regnancy.
aterials and methods
The Northern Congenital Abnormality Survey (NorCAS) is a rospective, population-based registry covering the former UK orthern health region, which includes north east England and orth Cumbria (Fig. 1). This region comprises a population of bout three million, with approximately 32,000 births each year ver the study period 2000–2008, of which approximately 826 irths each year (2.6%) included a major congenital anomaly nd were therefore recorded in NorCAS. Data are collected on ongenital anomalies occurring in late miscarriages (>20 weeks estation), in live births and stillbirths, and in terminations of regnancy for foetal anomaly after prenatal diagnosis at any estation. The NorCAS follows the European Surveillance of Con- enital Anomalies guidelines for inclusion on the register and lassification of anomalies (see http://www.eurocat-network.eu/ ontent/EUROCAT-Guide-1.3-Chapter-3.3-Jan2012.pdf) and codes nomalies according to the WHO International Classification of Dis- ases version 10. Cases are reported to the register from multiple ources to ensure a high case ascertainment, as described pre- iously (Boyd et al., 2005; Richmond and Atkins, 2005). For this tudy, data on all pregnancies with a congenital anomaly delivered etween 01 January 2000 and 31 December 2008 were extracted rom NorCAS, although this dataset was subsequently restricted to hose with a gestation at delivery of ≥24 weeks (a viable delivery), o allow better comparison with pregnancies resulting in a healthy elivery. If more than one baby in a multiple pregnancy has a con- enital anomaly, each case is included on NorCAS. However, for this tudy, the pregnancy was counted as the ‘case’ so each pregnancy as counted only once.
The NorCAS contains addresses for women at both booking ppointment (average gestational age 13 weeks in the UK) and elivery. To obtain more detailed information on residential his- ory, the NorCAS data were linked to the UK National Health Service ational Strategic Tracing Service records. Linkage was achieved sing several data fields, including the mother’s date of birth, ational Health Service number, surname and residential postcode. ddress at delivery was confirmed and updated as required. Date of onception was calculated from the date and gestation at booking available within the NorCAS), and address details at this date, as ell as any other residences during the index pregnancy (with dates
f when the women moved to and from this address) available from he National Strategic Tracing Service were extracted to provide ddress at conception, and enable residential history throughout regnancy to be established. All addresses were geocoded based n the address postcode centroid, the geographic centre of a col- ection of approximately 15 adjacent households making up theostcode. Within the study area the average distance between near- st neighbouring postcodes was 104 m, max 6.2 km, though this istance varied considerably between urban and rural areas (for xample, in Newcastle Local Authority (a predominantly urban
c http://uk-air.defra.gov.uk/data/pcm-data. d http://uk-air.defra.gov.uk/networks/network-info?view=aurn.
area) the average distance was 49 m, max 1.16 km, in contrast in Tynedale (rural authority) the average distance was 255 m, max 7.95 km). Grid references were obtained from the Office for National Statistics Postcode Directory (http://edina.ac.uk/ukborders/).
To establish the impact of residential mobility during pregnancy on exposure classification, we assigned to each woman’s postcode at delivery and conception a measure of exposure to a variety of environmental factors, and, based on residential history, a mea- sure of mean exposure throughout pregnancy weighted according to proportion of the pregnancy spent at each postcode. These vari- ables include typical environmental factors explored in aetiological epidemiological research. We deliberately chose factors that were (a) readily available, (b) varied in terms of their spatial and/or tem- poral resolution, and (c) able to be assigned at the individual and/or area level. These variables are described in Table 1.
For deprivation, we used the 2007 Index of Multiple Depriva-tion, which comprises 38 indicators of deprivation spread across seven domains (income deprivation; employment deprivation; health deprivation and disability; education, skills and traininghttp://www.eurocat-network.eu/content/EUROCAT-Guide-1.3-Chapter-3.3-Jan2012.pdfhttp://www.eurocat-network.eu/content/EUROCAT-Guide-1.3-Chapter-3.3-Jan2012.pdfhttp://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-100-m-version-8-2005http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-100-m-version-8-2005http://www.ordnancesurvey.co.uk/oswebsite/docs/user-guides/strategi-user-guide.pdfhttp://www.ordnancesurvey.co.uk/oswebsite/docs/user-guides/strategi-user-guide.pdfhttp://uk-air.defra.gov.uk/data/pcm-datahttp://uk-air.defra.gov.uk/networks/network-info?view=aurnhttp://edina.ac.uk/ukborders/
416 S. Hodgson et al. / International Journal of Hygiene and Environmental Health 218 (2015) 414–421
F ty Surv U
d m e c A 1 S c
p l a s w I c c c b f ( t a p p
S w a o
ig. 1. Map showing the geographic coverage of the Northern Congenital Abnormali rban and Rural Network NO2 monitors (black triangles).
eprivation; barriers to housing and services; living environ- ent deprivation; and crime) (Noble et al., 2008). This index was
xtracted at the area level for (1) lower layer Super Output Areas, a ensus based unit with a mean population of 1500, and (2) at Local uthority level, an administrative unit with a mean population of 40,000. We assigned to each postcode the deprivation score of the uper Output Area or Local Authority that contained that postcode entroid.
The CORINE land cover classes are discriminated mainly by hysiognomic attributes (shape, size, colour and pattern) of
andscape objects (natural, modified, cultivated and artificial), s recorded on satellite images (de Lima, 2005). The smallest urfaces mapped correspond to 25 ha, and the scale of the output as fixed at 1:100,000, giving a location precision of 100 m.
n the Continuous urban fabric land class, most of the land is overed by buildings, roads and artificially surfaced areas which over almost all the ground. The Discontinuous urban fabric land lass is also characterised by most of the land being covered y structures, but here the buildings, roads and artificially sur- aced areas are associated with vegetated areas and bare soil www.eea.europa.eu/publications/COR0-part1/download). Con- inuous urban fabric (3) and Discontinuous urban fabric (4) were ssessed at the individual level; we assigned to each postcode the roportion of each land cover class within a 500 m buffer of the ostcode centroid.
For roads (5), assessed at the individual level, we used OStrategi data to assign to each postcode the total metres of motor- ays, A roads (large-scale transport links within or between areas)
nd B roads (which feed traffic between A roads and smaller roads n the network) within a 500 m buffer of the postcode centroid.
ey (NorCAS) (shaded area), and inset showing the locations of the DEFRA Automatic
For annual background particulate matter (particles less than 10 �m in diameter (PM10)) (6), we used DEFRA Ambient Air Quality data (background pollution maps at 1 km × 1 km resolu- tion) to assign to each postcode at conception and delivery the annual mean PM10 concentration for year of conception. To calcu- late the mean PM10 exposure through pregnancy, each postcode was assigned the annual mean(s) for the year(s) of residence, which were then weighted according to proportion of the pregnancy spent at each postcode.
Nearest monitor daily mean nitrogen dioxide (NO2) concentra- tions (7), a variable with limited spatial variability due to the small number of monitors across the study region (at conception, six sites in the north east provided data for 98.4% of the women, see Fig. 1), was assessed at the individual level (for those women living within 15 km of a monitor (an arbitrary cut-off)). We used DEFRA Auto- matic Urban and Rural Network data (the main network used for compliance reporting) to assign to each postcode at delivery and conception the mean NO2 exposure for the first trimester (first 90 days of each pregnancy), as well as mean exposure throughout pregnancy based on residential history.
The level of agreement between (a) exposure at conception assigned to postcode at delivery versus postcode of conception, and (b) exposure at conception assigned to postcode at delivery versus mean exposure throughout pregnancy based on residential history, was assessed by a range of measures. These included: (i) as continuous variables using Pearson correlation co-efficient (R),(ii) as quintiles using Cohen’s kappa co-efficient (K) to take into account agreement occurring by chance, with quintiles based on equal percentiles at conception/mean exposure throughout preg- nancy, apart from continuous urban fabric which, due to granularityhttp://www.eea.europa.eu/publications/COR0-part1/download
S. Hodgson et al. / International Journal of Hygiene and Environmental Health 218 (2015) 414–421 417
Table 2 Agreement between exposures (a) at conception, assigned to postcode at delivery versus postcode at conception, and (b) at conception assigned to postcode at delivery versus mean through pregnancy based on residential history, for all women, non-movers and those who moved during pregnancy.
Variable All women Non-movers Movers
n R K Accuracy n R K Accuracy n R K Accuracy
1. Super Output Area Deprivation Score a) Delivery versus conception 5391 0.89 0.83 0.91 4078 1 1 1 1313 0.57 0.30 0.62 b) Delivery versus mean through pregnancy 5396 0.96 0.90 0.95 4078 1 1 1 1318 0.86 0.59 0.81
2. Local Authority Deprivation Score a) Delivery versus conception 5391 0.92 0.94 0.97 4076 1 1 1 1313 0.69 0.75 0.89 b) Delivery versus mean through pregnancy 5393 0.98 0.96 0.98 4076 1 1 1 1317 0.90 0.84 0.94
3. % Continuous Urban Fabric a) Delivery versus conception 5399 0.81 0.83 0.96 4080 1 1 1 1319 0.33 0.32 0.84 b) Delivery versus mean through pregnancy 5399 0.94 0.91 0.97 4080 1 1 1 1319 0.75 0.68 0.89
4. % Discontinuous Urban Fabric a) Delivery versus conception 5399 0.84 0.79 0.90 4080 1 1 1 1319 0.34 0.19 0.59 b) Delivery versus mean through pregnancy 5399 0.95 0.88 0.94 4080 1 1 1 1319 0.79 0.49 0.77
5. Metres roads within 500 m a) Delivery versus conception 5399 0.83 0.80 0.88 4080 1 1 1 1319 0.36 0.20 0.52 b) Delivery versus mean through pregnancy 5399 0.94 0.86 0.93 4080 1 1 1 1319 0.78 0.44 0.71
6. Annual PM10 a) Delivery versus conception 4396 0.95 0.91 0.96 3290 1 1 1 1106 0.81 0.65 0.85 b) Delivery versus mean through pregnancy 4396 0.88 0.64 0.84 3290 0.90 0.66 0.86 1108 0.81 0.55 0.80
7. NO2 a) Delivery versus conception 3373 0.95 0.98 0.98 2571 1 1 1 802 0.81 0.89 0.92 b) Delivery versus mean through pregnancy 3365 0.74 0.27 0.75 2571 0.76 0.26 0.75 794 0.67 0.28 0.73
R = Pearson correlation co-efficient, with exposures assessed as continuous variables. K rban A f expo
i o p c w a
m p I e p t n s
t ( f t (
7 t 2 m G S g t
= Cohen’s kappa co-efficient, with exposures as quintiles, apart from continuous u ccuracy = exposure at delivery assumed correct if within one standard deviation o
n the data, was explored as a dichotomous variable (i.e. exposed r not exposed to any continuous urban fabric within 500 m of ostcode), or (iii) assessed for accuracy, where exposure at con- eption assigned to delivery postcode was assumed ‘correct’ if it as within one standard deviation (SD) of the exposure assigned
t conception postcode/mean exposure throughout pregnancy. To explore the likelihood of introducing differential exposure
isclassification, independent sample t-tests were used to com- are mean exposure at conception for non-movers versus movers.
n addition, paired sample t-tests were used to compare mean xposure at conception assigned to postcode of delivery versus ostcode of conception, and mean exposure at conception assigned o postcode of delivery versus mean exposure throughout preg- ancy based on residential history. p Values <0.05 were taken as tatistically significant.
Data were linked in GIS ESRI ArcMap 10.0 and analysed using BM SPSS Statistics Version 20.
The NorCAS, as part of the British Isles Network of Congeni- al Anomaly Registers, has National Information Governance Board now Health Research Authority) exemption from a requirement or consent for inclusion on the register under section 251 of he National Health Service Act (2006) and has ethics approval 09/H0405/48) to undertake studies involving the use of its data.
NorCAS registered 7432 deliveries during 2000–2008. Of these, 231 (97.3%) were able to be linked to women represented in he National Strategic Tracing Service data, with the remaining 01 deliveries not able to be linked, likely due to missing or mis- atched data, or due to their mother not being registered with aP and therefore not appearing in the National Strategic Tracing ervice dataset. Postcode at conception and delivery was able to be eocoded for 6972/7432 deliveries (93.8%). When further restricted o represent pregnancies with a gestational age at delivery of ≥24
fabric which was explored as a dichotomous variable. sure assigned at conception/throughout pregnancy.
weeks (a viable delivery), 5399 (72.7%) pregnancies remained. Of these, 1319 women (24.4%) moved during pregnancy. With respect to the timing of moves, the mean number of days after gestation before the first move was 112 days (16 weeks); a little over half of the women who moved (686/1319; 52%) did so during their first trimester, 378 (28.7%) moved during their second trimester, and 255 (19.3%) moved during their third trimester. The mean and median moving distance amongst movers were short, at 19.26 and 1.85 km respectively, with 72.5% of women moving within 5 km.
When looking at all women, the majority of whom did not move, there was, as expected, good agreement between (a) expo- sure at conception assigned to postcode at delivery versus postcode at conception, and (b) exposure at conception assigned to post- code at delivery versus mean exposure throughout pregnancy based on residential history (Table 2). The level of agreement was similar when variables were assessed using Pearson correlation co- efficient (R), Cohen’s kappa co-efficient (K) or assessed for accuracy (i.e. within one standard deviation (SD)). For the air quality vari- ables PM10 (6) and NO2 (7), which exhibit temporal variability, the agreement between exposure at delivery and mean through- out pregnancy was weaker, likely due to the underlying temporal trends in pollution levels, which showed a decline over the time period studied. For women who moved during pregnancy, the agreement between exposures at conception assigned to postcode at delivery versus conception, or postcode at delivery versus mean exposure throughout pregnancy was much weaker.
The relatively good agreement, overall, between exposures at conception assigned to delivery versus conception postcode, and at delivery postcode versus residences throughout pregnancy, hides the fact that, at the individual level, substantial differences in expo- sure do occur.
For some variables, a substantial proportion of women would be assigned a different exposure at conception if postcode at delivery was used in lieu of postcode at conception, or in lieu of residen-tial history throughout pregnancy. Fig. 2 shows the difference in exposure at conception assigned to maternal postcode at delivery versus conception, and, for PM10, the difference in exposure at con- ception assigned to maternal postcode at delivery versus exposure
418 S. Hodgson et al. / International Journal of Hygiene and Environmental Health 218 (2015) 414–421
Fig. 2. Histograms showing the difference in exposure at conception assigned to maternal postcode at delivery versus conception for all women (left hand side) and movers (right hand side) for: (1) Deprivation at Super Output Area level, (2) Deprivation at Local Authority level, (6a) PM10; and (6b) the difference in PM10 exposure at conception assigned to maternal postcode at delivery versus exposure throughout pregnancy based on residential history.
S. Hodgson et al. / International Journal of Hygiene and Environmental Health 218 (2015) 414–421 419
F ion an A
t a s A m a w e m d ( s t ( n c F t e i
t e m d a
t t i (
p r a o a d r W n
ig. 3. Change in Deprivation Score (in SD) between maternal postcode at concept rea, and (2) Deprivation Score assigned to Local Authority.
hroughout pregnancy based on residential history, for all women nd for those moving during pregnancy. For spatially varying expo- ures assigned at the area level (e.g. Deprivation at the Super Output rea (1) and Local Authority level (2)) or individual level (e.g. annual ean PM10 (6a)) we see that, overall, relatively few women are
ssigned a different exposure. In these instances, it is only those omen moving during pregnancy who would be assigned a differ-
nt exposure. It is evident that the scale at which the exposure is easured is important; far more women are assigned a different
eprivation score when measured at the Super Output Area level 1) than at the Local Authority level (2). Where women move only hort distances (>70% within 5 km), they are more likely to move o a different Super Output Area than to a different Local Authority Fig. 3). Nonetheless, for those women who do move during preg- ancy, their exposure at conception assigned to delivery postcode an be quite different from that assigned to conception postcode. or exposures with spatial and temporal variability (PM10, NO2), here were substantial differences in exposure at conception versus xposure throughout pregnancy, for all women and for those mov- ng during pregnancy (e.g. Fig. 2 (6b)).
While our previous study showed that movers in this cohort ended to be younger and to live in more deprived areas (Hodgson t al., 2009), independent sample t-tests show that, at conception, overs tend to live in more deprived, urban areas, near to a greater
ensity of roads, and with lower air quality (as measured by PM10 nd NO2) (Table 3).
Furthermore, as shown in Table 4, movers tended, on average, o move to less deprived, less urban areas (although paired sample -tests indicate that these differences were not statistically signif- cant), with lower road density (p = 0.05), and higher air quality >0.01).
We investigated the impact of residential mobility during regnancy on how exposure to a range of real-life social and envi- onmental factors, which exhibited spatial and temporal variability t a range of scales, is characterised. We aimed to assess the degree f exposure error/misclassification that might be introduced into
study using address at delivery as a proxy for maternal resi-ence (and, therefore, foetal exposure) at a more aetiologically elevant period, such as at conception, or throughout pregnancy.
e have shown that mean exposures, even amongst movers, may ot significantly differ when assigned to address at delivery versus
d delivery by distance moved for (1) Deprivation Score assigned to Super Output
conception. However, comparing mean exposures hides the fact that increases in exposure in some are offset by decreases in oth- ers; depending on the scale at which exposure is measured and/or the scale at which it exhibits heterogeneity, substantial numbers of women may in fact have been assigned very different exposures at delivery versus conception address.
Previous studies addressing the issue of mobility have reported that exposure does not differ significantly if using maternal res- idential address at delivery rather than address at conception, implying that use of the former is adequate to estimate exposure during the critical early stages of pregnancy (Chen et al., 2010; Lupo et al., 2010). Chen et al. (2010) explored the impact of mobility on exposure to ozone and PM10 in 1324 women in New York, 16.5% of whom moved during pregnancy. There was a good agreement between exposure quartiles measured at conception and delivery (Kappa ≥0.78, p < 0.01), however the spatial resolution of the expo- sure data was low; the study area was divided into only seven air monitoring regions of between 247–11,790 and 628–10,760 square miles for ozone and PM10 respectively. The scale at which the exposure data were available may have played a role in shap- ing the observed agreement between these exposure variables at conception and delivery; very few women (n = 33) moved between monitoring regions (Chen et al., 2010). Lupo et al. (2010) explored the impact of mobility on the assignment of census tract-level esti- mates of ambient benzene at the delivery and conception addresses of 141 pregnancies affected by a neural tube defect and 591 unaf- fected control pregnancies. Although 30% of case and 24% of control mothers moved during pregnancy, there was good agreement between quartiles of benzene exposure at delivery and concep- tion across the study population (Kappa = 0.78, p < 0.01), which the authors attributed to the fact that the residential movements were generally within a short distance. Nonetheless, 17% of women were misclassified (if we take exposure at conception to be the gold standard), and 4.5% were misclassified by two or more quartiles (Lupo et al., 2010).
The theoretical papers on this topic discuss the issue of whether residential mobility is likely to introduce non-differential or dif- ferential exposure error. Ritz et al. (2007) found the association between CO exposure and preterm birth strengthened (although confidence intervals widened) when their analyses were restrictedto women who had not changed residence throughout pregnancy, suggesting that non-movers suffer less from exposure misclassifi- cation/error, and that in this instance, the misclassification/error was likely to be non-differential. Madsen et al. (2010) studied
420 S. Hodgson et al. / International Journal of Hygiene and Environmental Health 218 (2015) 414–421
Table 3 Exposure at conception for women who did not, versus those who did, move during pregnancy.
Exposure Non-movers Movers pa
n Mean n Mean
1. Super Output Area Deprivation Score 4078 31.75 1314 35.13 <0.01 2. Local Authority Deprivation Score 4076 28.15 1314 28.17 0.91 3. % Continuous Urban Fabric within 500 m 4080 4.61 1319 5.61 <0.05 4. % Discontinuous Urban Fabric within 500 m 4080 62.51 1319 63.21 0.45 5. Metres roads within 500 m 4080 734.25 1319 782.21 0.03 6. Annual PM10 (�g/m3) 3290 14.34 1106 14.64 <0.01
e 2 p p w i l m b w m k 2 d T c c l g m r o
t t h s e i L r t e m s e 2
T E t
7. First trimester NO2 (�g/m3) 2571
a Independent sample t-tests.
xposure misclassification in a cohort of 25,229 pregnant women, 8% of whom moved during pregnancy. Women who moved during regnancy had a lower traffic pollution exposure after moving com- ared to women who did not move. In addition, women who moved ere younger, more often nullparious and of non-western ethnic-
ty, had lower education, and their offspring had on average 47.5 g ower mean birth weight compared to those women who did not
ove during pregnancy (Madsen et al., 2010) suggesting the possi- ility of differential exposure error. In the NorCAS cohort, women ho moved during pregnancy also tended to be younger and live in ore socio-economically deprived areas (Hodgson et al., 2009) in
eeping with findings from other populations (Bell and Belanger, 012), and, as shown here, movers also tend to live near a higher ensity of roads and in areas with higher levels of air pollution. hese women tend to have lower exposure at delivery than at con- eption. If women are moving from areas with higher exposure at onception to areas with lower exposure at delivery, then it seems ikely that using postcode at delivery will result in exposure in this roup being underestimated, and, if the characteristics of those who ove are associated with the health outcome of interest, then we
isk introducing differential bias into our study, potentially biasing ur risk estimates towards or away from the null.
The extent of exposure error introduced by assigning exposure o the address at delivery will depend on the degree of spatial and emporal variability the exposure exhibits, the scale at which this eterogeneity acts, and the resolution at which the exposure is tudied. Residential mobility is less likely to introduce exposure rror into a study assessing exposures which display heterogene- ty over a large geographic scale (e.g. Deprivation measured at the ocal Authority area (2) versus Super Output Area level (1)). Until ecently it could have been argued that the spatial/temporal resolu- ion of our exposure data was the limiting factor in environmental pidemiological studies and that the bias introduced by residentialobility only a minor concern. We are currently experiencing a
tep change in our ability to measure and/or model environmental xposures over large areas and at a high resolution (Beelen et al., 009; Vienneau et al., 2010), but the benefits of such improvements
able 4 xposure at conception assigned to postcode at delivery versus postcode at conception, a hroughout pregnancy based on residential history, for women who moved during pregn
Exposure n Mean exposure
1. Super Output Area Deprivation Score 1313 35.11 2. Local Authority Deprivation Score 1313 28.17 3. % Continuous Urban Fabric 1319 5.61 4. % Discontinuous Urban Fabric 1319 63.21 5. Metres roads within 500 m 1319 782.21 6. Annual PM10 (�g/m3) 1106 14.64 7. NO2 (�g/m3) 802 29.12
a Paired sample t-test of difference between exposure at conception assigned to postco b Paired sample t-test of difference between exposure at conception assigned to postco
28.36 838 29.22 <0.01
will only be fully realised once we are better able to capture details of how people interact with this exposure surface. Capturing and incorporating better data on aetiologically relevant exposure periods, and on where people reside at these times is a first, but important step, in linking people to the changing exposure envi- ronment they inhabit.
There are several limitations to consider when interpreting our findings. Firstly, the cohort studied was derived from a congeni- tal anomaly dataset, so these findings may not be generalisable to all pregnancies, the majority of which result in a healthy infant. That said, there is no particular reason to believe these women will behave very differently with respect to residential mobility com- pared to women experiencing healthy pregnancy outcomes in the wider population from which our sample was drawn, as supported by evidence from case–control studies where mobility was sim- ilar for cases and controls (Bell and Belanger, 2012), and from a re-analysis of these data restricted to term deliveries (≥37 weeks) which produced essentially the same output (data not shown). Sec- ondly, we have focussed on exposure error/misclassification due to residential mobility. It should be noted that this is only one aspect of a much wider issue relating to measuring and assigning meaningful exposures. Research questions obviously vary study by study, but we are usually trying to assess the impact of the biologically rele- vant dose at the target organ of interest (Blair et al., 2007). Given the difficulty of achieving this ideal, certainly in large populations and for exposures for which we have no or inadequate biological mark- ers, we compromise and instead try to associate environmental concentrations near (or often rather far) from where someone lives (at birth, diagnosis, hospitalisation, death) to the health outcome of interest. This approach fails to account for inward or outward migration, or for daily or periodic spells away from a residence where levels of exposure are likely to differ from those experienced at home. Clearly there are many layers of exposure error introducedinto this scenario before we start to concern ourselves about where someone actually lived at the aetiologically critical time.
Detailed data on residential histories are not routinely collected in the UK, despite their value for linking environmental exposures
nd exposure at conception assigned to postcode at delivery versus mean exposure ancy.
Delivery pa Pregnancy pb
34.92 0.70 34.99 0.87 28.39 0.11 28.21 0.01
5.15 0.37 5.52 0.21 63.08 0.89 63.12 0.94
738.30 0.05 754.39 0.20 14.42 <0.01 14.46 0.36 28.61 <0.01 28.46 0.43
de at delivery versus conception. de at delivery versus mean exposure throughout pregnancy.
a s v W d d e u a t a i c m i t s e o s
m C R
P r c ( s c S E A t ( a L
S. Hodgson et al. / International Journal of Hyg
nd health. These findings highlight the importance of collecting uch data, and we encourage health registers to introduce rele- ant data fields to enable these data to be collected in the future. here collecting data on full residential histories is not possible,
ata on length of time in residence, or whether a move took place uring the time window of interest (e.g., during pregnancy) would nable an assessment of the potential impact of mobility on meas- res of risk. Where data on mobility are not available, researchers re urged to consider the likely impacts of residential mobility on heir exposure estimates, for instance by considering the spatial nd temporal heterogeneity of the exposure, and the scale at which t is measured. An understanding of the study population is also rucial, as for certain health end points e.g. those associated with aternal age, socio-economic deprivation etc., there is a possibil-
ty that mobility will introduce differential misclassification. Given he complex issues outlined above, pregnancy offers a relatively traight forward opportunity to study the impact of mobility on xposure error; for risk factors that may confer a health risk years r even decades after exposure, it is even more important that these ources of exposure error are studied and their impacts understood.
The NorCAS is funded by the UK Healthcare Quality Improve- ent Partnership. JR was funded by a Personal Award Scheme
areer Scientist Award from the National Institute of Health esearch (UK Department of Health).
We are grateful to all the Link Clinicians in the Northern region or their continued collaboration and support of the NorCAS, and hank Daniela Fetch for her useful comments on the manuscript.
We acknowledge the use of the UK Office for National Statistics ostcode Directory (Contains Ordnance Survey data © Crown copy- ight and database right 2012 and National Statistics data © Crown opyright and database right 2012); 2001 Digitised Boundary Data England and Wales) (Office for National Statistics, 2001 Cen- us, Output Area Boundaries. Crown copyright 2003); Corine land over 2000 (Copyright holder: European Environment Agency); trategi 2011 (OS Strategi Crown Copyright Ordnance Survey. An DINA Digimap/JISC supplied service). Also: Ambient Air Quality ssessments (UKAAQA) and Automatic Urban and Rural Moni-
oring Network (AURN) data (Defra), UK Mean Temperature data Met Office), and English Indices of Deprivation 2007 (Communities nd Local Government), all licensed under the Open Government icence v1.0.
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- Exposure misclassification due to residential mobility during pregnancy
- Materials and methods
- Ethical approval