epidemiologist Schieve La
email: dh@soci.au.dk

Risk of Autism Spectrum Disorders in children born after assisted conception. A population based follow-up study

Objectives: to assess the risk of Autism Spectrum Disorders (ASD) in children born after assisted conception compared to children born after natural conception (NC). Design: population based follow-up study. Setting: all children born alive in Denmark 1995-2003. Participants: 588,967 children born in Denmark from January 1995 to December 2003. Assisted conception was defined as in vitro fertilization (IVF), with or without intracytoplasmic sperm injection (ICSI) and ovulation induction (OI) with or without subsequent insemination. Children exposed to IVF or OI were identified in the IVF Register and in the Danish Drug Prescription Register. Main outcome measures: a diagnosis of Autism Spectrum Disorders in the Danish Psychiatric Central Register. Results: 33,139 (5.6%) of all children born in Denmark 1995-2003 resulted from assisted conception and 225 (0.68%) of these exposed children received an ASD diagnosis. Of the 555,828 NC children born in this period 3,394 (0.61%) received an ASD diagnosis. The follow-up time was 4 to 13 years with a median follow-up time of 9 years. In crude analyses children born after assisted conception had an increased risk of getting an ASD diagnosis, crude hazard rate ratio (HRR) 1.25 (95% CI 1.09-1.43). In analyses adjusting for maternal age, educational level, parity, smoking, birth weight and multiplicity the risk disappeared, adjusted HRR 1.13. (95% CI 0.97-1.31). However, subgroup analyses that suggest possible associations in women who received FSH medications indicate the need for further study. Discussion: this population based follow-up study demonstrated no risk of ASD in children born after assisted conception.

What is already known on this subject?

• The few studies that have assessed the association between Autism Spectrum Disorders (ASD) and assisted conception present inconsistent results and disclose methodological limitations such as lack of statistical power. • Associations between ASD and assisted conception might be anticipated due to at least 3 shared risk factors; high parental age, high maternal educational level and hormonal disturbances. • Furthermore, a higher risk of ASD in children born after assisted conception might also be expected since assisted conception leads to a high rate of preterm delivery (PTD) and low birth weight (LBW) and associations between PTD or LBW and ASD have been reported.

What this study adds

• This population based follow-up study demonstrated no risk of ASD in children born after assisted conception.

• However, we did find an increased risk in a subgroup of women who had IVF treatment and were exposed to FSH

Introduction

The etiology behind infantile autism and autism spectrum disorders (ASD) is multifactorial and largely unknown. [START_REF] Baron | Autism Spectrum Disorder: Complex, Controversial, and Confounding[END_REF] Associations between ASD and assisted conception might be anticipated due to at least 3 shared risk factors; high parental age, [START_REF] Durkin | Advanced parental age and the risk of autism spectrum disorder[END_REF][START_REF] Croen | Descriptive epidemiology of autism in a California population: who is at risk?[END_REF][START_REF] Durkin | Advanced parental age and the risk of autism spectrum disorder[END_REF][START_REF] Glasson | Perinatal factors and the development of autism: a population study[END_REF][START_REF] Larsson | Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status[END_REF][START_REF] Lauritsen | Effects of familial risk factors and place of birth on the risk of autism: a nationwide register-based study[END_REF][START_REF] Reichenberg | Advancing paternal age and autism[END_REF] high maternal educational level 3;9 and hormonal disturbances. [START_REF] Auyeung | Fetal testosterone and autistic traits[END_REF][11][START_REF] Geier | A clinical and laboratory evaluation of methionine cycletranssulfuration and androgen pathway markers in children with autistic disorders[END_REF] Furthermore, a higher risk of ASD in children born after assisted conception might also be expected since assisted conception leads to a high rate of preterm delivery (PTD) and low birth weight (LBW) [START_REF] Helmerhorst | Perinatal outcome of singletons and twins after assisted conception: a systematic review of controlled studies[END_REF][START_REF] Nyboe | Results generated from European registers by ESHRE[END_REF][START_REF] Kallen | Neonatal outcome in pregnancies from ovarian stimulation[END_REF][START_REF] Wang | The effect of various infertility treatments on the risk of preterm birth[END_REF][START_REF] Olivennes | The increased risk of complication observed in singleton pregnancies resulting from in-vitro fertilization (IVF) does not seem to be related to the IVF method itself[END_REF][START_REF] Gaudoin | Ovulation induction/intrauterine insemination in infertile couples is associated with low-birth-weight infants[END_REF][START_REF] Ombelet | Perinatal outcome of 12,021 singleton and 3108 twin births after non-IVF-assisted reproduction: a cohort study[END_REF] and associations between PTD or LBW and ASD have been reported. 3;6;20;21 The use of assisted conception has increased over the last 2 decades. 14;22 We identified 8 studies assessing the risk of infantile autism or ASD in children born after assisted conception. [START_REF] Lidegaard | Imprinting diseases and IVF: Danish National IVF cohort study[END_REF][START_REF] Ericson | Hospital care utilization of infants born after IVF[END_REF][START_REF] Stromberg | Neurological sequelae in children born after in-vitro fertilisation: a population-based study[END_REF][START_REF] Pinborg | Morbidity in a Danish national cohort of 472 IVF/ICSI twins, 1132 non-IVF/ICSI twins and 634 IVF/ICSI singletons: health-related and social implications for the children and their families[END_REF][START_REF] Pinborg | Neurological sequelae in twins born after assisted conception: controlled national cohort study[END_REF][START_REF] Klemetti | Health of children born as a result of in vitro fertilization[END_REF][START_REF] Stein | Obstetric complications in individuals diagnosed with autism and in healthy controls[END_REF][START_REF] Maimburg | Do children born after assisted conception have less risk of developing infantile autism?[END_REF] They present inconsistent results and disclose methodological problems such as lack of statistical power [START_REF] Pinborg | Morbidity in a Danish national cohort of 472 IVF/ICSI twins, 1132 non-IVF/ICSI twins and 634 IVF/ICSI singletons: health-related and social implications for the children and their families[END_REF][START_REF] Pinborg | Neurological sequelae in twins born after assisted conception: controlled national cohort study[END_REF][START_REF] Klemetti | Health of children born as a result of in vitro fertilization[END_REF] or only evaluating a combined group of psychiatric disorders, rather than ASD specifically. 28;31 Furthermore, 6 studies only assessed one type of assisted conception, in vitro fertilization (IVF). In all, only one study found a significantly higher occurrence of ASD in children born after assisted conception compared to children born after natural conception (NC) [START_REF] Pinborg | Morbidity in a Danish national cohort of 472 IVF/ICSI twins, 1132 non-IVF/ICSI twins and 634 IVF/ICSI singletons: health-related and social implications for the children and their families[END_REF] ; however, four other studies reported a non-statistically significantly increased risk. 23;24;28;29 One case-control study reported a protective effect of assisted conception on infantile autism after adjusting for several factors including gestational age. [START_REF] Maimburg | Do children born after assisted conception have less risk of developing infantile autism?[END_REF] None of the studies distinguished between different underlying causes of infertility or between different types of medications used in the treatment. Consequently, the relationship between ASD and assisted conception is still not well understood. To overcome methodological problems in former studies, we assessed the risk of infantile autism and ASD in a large population-based cohort of children born after assisted conception.

Assisted conception was defined as IVF, with or without intracytoplasmic sperm injection (ICSI) and ovulation induction (OI) with or without subsequent insemination. We also evaluated underlying causes of infertility and specific medications used in the treatments.

Methods

Study population

This cohort study was based on data from Danish National Registers and linkage between the registers was achieved by use of the unique registration number given to all citizens in Denmark. Our study comprised all children born alive in Denmark from 1 January 1995 to 31 December 2003, identified through the Danish Medical Birth Register (MBR), containing information on all births in Denmark. [START_REF] Knudsen | The Danish Medical Birth Registry[END_REF]

Exposure

Children exposed to IVF were identified through the IVF Register holding data from all private and public fertility clinics including underlying causes of infertility (tubal, male, ovulation or mixed and unspecified factors). [START_REF] Andersen | The Danish in vitro fertilisation (IVF) register[END_REF] Children exposed to OI were identified through the Danish Drug Prescription Register (DDPR) holding information on all prescription drugs sold at pharmacies in Denmark. The medications used during OI are prescription drugs bought at the pharmacy enabling identification of the women who went through OI in the DDPR. Drugs used in assisted conception were identified by the authors through cross checking with the official Danish Pharmaceutical Classified Catalogue, www.medicin.dk, the US website www.drugs.com, Books of Instruction of Danish fertility clinics from the time period in question and by means of clinical experience. To identify women who had hormonal treatment in relation to the index pregnancy, we set up a time window for the date of dispatch of 12 weeks before and 4 weeks after last menstrual period (LMP). As the drugs used in OI are also used in IVF, we excluded women from the OI group who were included in the IVF register with the same LMP date.

Outcome

Children diagnosed with an ASD diagnosis (F84.0, F84.1, F84.5, F84.8 and F84.9) or specifically infantile autism (F84.0) through May 8, 2008 were identified via the Danish Psychiatric Central Register (DPCR). The DPCR implements information on all Danish psychiatric in-and out-patient admissions since 1995 [START_REF] Munk-Jorgensen | The Danish Psychiatric Central Register[END_REF] and in Denmark all autism diagnoses are made at public child mental health services, reporting all in-and outpatient discharge diagnoses to the DPCR.

Covariates

Multivariate analyses

Cox regression was used to measure the risk of getting an ASD diagnosis in children born after assisted conception combined and in IVF and OI separately. We conducted multivariate analyses including maternal age, parity, smoking, educational level, BW and multiplicity. Due to the skewed sex distribution in ASD, analyses were conducted for all children and stratified by sex.

Sub-analyses

In further analyses we explored if the risk of ASD was associated with a single underlying cause of infertility (tubal, male, ovulation or mixed and unspecified factors), but this information was only available in the IVF Register and only for 9,855 (66%) of the IVF children as many of the IVF children had unspecific or more than one cause. Finally, to disentangle if different hormones used in assisted conception (down-regulating hormones, clomiphene, follicle-stimulating hormone (FSH) and human chorionic gonadotropin (hCG)) were associated with ASD, we made exploratory analyses with specific hormone exposure. Information about use of specific hormones was available from the DDPR for all OI children but only for 51% of the IVF children.

This was due to different dispensing procedures at IVF clinics; some clinics dispense the medication directly to the women while other IVF clinics provide prescriptions for the pharmacy. Only the latter group of women is included in the DDPR.

Statistics

The risk of getting an ASD or infantile autism diagnosis was measured by the incidence rate, using the child as the unit of analysis. The incidence rate was defined as the number of children receiving an ASD diagnosis in a given time frame (1 January 1995 -8 May 2008) divided by the total number of person-years the children in the cohort were at risk for the diagnosis during that time. We used Cox regression and the incidence rate ratio was estimated by the hazard rate ratio (HRR) with 95% confidence intervals (CI). It was verified that the proportional hazards assumption was met. To account for correlations between siblings in the cohort additional analyses were done employing robust standard errors. To reduce bias that might arise if exposed children at high risk for ASD were more likely to die as infants than unexposed children we conducted additional analyses excluding children less than one year old (i.

Results

From January 1 1995 to December 31 2003, 588,967 children were born alive in Denmark from 399,598 mothers of 20 years or more. In this period 33,139 (5.6%) children resulted from assisted conception; 14,991 (2.6%) from IVF and 18,148 (3.0%) from OI. Compared to NC mothers, mothers having assisted conception were statistically significantly older (P <0.001); more often primipara (P <0.001); non-smokers (P <0.001); they had higher educational level (P 0.002) and delivered more multiples (P <0.001), children with low BW (P <0.001) and PTD (P <0.001) (Table 1). Furthermore, mothers having IVF, as opposed to OI, had the largest difference from mothers who conceived naturally on most of these characteristics. (Table 1) educational level, smoking, parity, BW and multiplicity we found a HRR of 1.13 (0.97-1.31) in the combined assisted conception group and higher risk estimates in OI than in IVF (Table 3). In the adjusted analyses only girls born after OI had a statistically significant increased risk of ASD. Similar results were seen for infantile autism, however with wider confidence intervals (data not shown). The risk of ASD for boys and girls did not differ statistically significant. Restricting analyses to infants who survived their first year showed similar results to the full cohort (data not shown). Dropping variables from the adjusted analyses one at a time showed that the increased risk in the crude analyses seemed to be explained by maternal age and parity but not by smoking, multiplicity, BW and educational level. The HRR in an adjusted analyses not including maternal age was 1.24 (1.06-1.44) and in an adjusted model without parity the HRR was1.28 (1.10-1.49).

For 9,855 IVF children we had information on a single underlying cause of infertility. The risk of ASD was highest for those children with a maternal infertility diagnosis of ovulation factor; however, estimates are imprecise due to the small sample sizes. (Table 4) Information on specific hormonal medications used in assisted conception was available for 25,867 children and in the analyses of the risk of ASD by different hormone exposure the association with ASD was strongest with medications containing FSH; and not clearly evident for other hormonal medications. (Table 5) Divided into specific treatment type, the FSH associations were observed only within IVF treatments. The risk was higher for girls than for boys across all hormones and statistically significant for FSH, hCG and clomiphene. We were able to assess FSH use in all OI treatments but only in 51% of the IVF treatments as we could not confirm the specific medications used through the DDPR for the remaining IVF treatments.

Women who underwent IVF and had detailed medication data from DDPR were different from those women without DDPR data in that they were older, more often multipara, non-smokers, more highly educated, and more likely to have been treated at a private rather than public clinic. They were also more likely to have ovulation factor infertility, however, even with this increase, it is important to note that while the vast majority of women who underwent IVF and had detailed medication data from DDPR used FSH (85%), only a small minority (9%) had documentation of ovulation factor infertility. We were unable to assess both factors simultaneously because of limited sample size (only 58 cases had data on both single underlying cause of infertility and specific hormone treatment).

Discussion

Main Results

In this large population based cohort study we found no risk of ASD in children born after assisted conception, the adjusted HRR was 1.13 (95% CI 0.97-1.31).The increased risk for ASD observed in unadjusted analyses seemed to be explained by high maternal age and parity but did not seem to be explained by educational level, low BW, smoking or multiplicity. This is the largest population based study to date, including 33,139 children exposed to assisted conception and hence the first study also permitting assessment of different types of assisted conception separately. In the present study the prevalence of ASD was 6 per 1000 children, similar to reports from other studies with ASD prevalence of 4.7, 5.8 and 6-7 per 1000. [START_REF] Tebruegge | Does routine child health surveillance contribute to the early detection of children with pervasive developmental disorders? An epidemiological study in Kent, U.K[END_REF][START_REF] Chakrabarti | Pervasive developmental disorders in preschool children: confirmation of high prevalence[END_REF][START_REF]Prevalence of Autism Spectrum Disorders. Surveillance summaries[END_REF]

Sex, maternal age, parity and birth weight

The risk from assisted conception tended to be higher in girls than in boys in all strata. Other studies suggest that the etiologic profile behind ASD in girls and in boys may be different. 38 Furthermore, since the a priori risk in boys is so much higher than the risk in girls it is possible that ASD in boys represents either a more heterogeneous mix of autism etiology subtypes or a mix of potential etiologies which could moderate an association of assisted conception with ASD.

The risk of autism or ASD is associated with advanced maternal age in some studies with adjusted analyses [START_REF] Durkin | Advanced parental age and the risk of autism spectrum disorder[END_REF][START_REF] Croen | Descriptive epidemiology of autism in a California population: who is at risk?[END_REF][START_REF] Durkin | Advanced parental age and the risk of autism spectrum disorder[END_REF][START_REF] Glasson | Perinatal factors and the development of autism: a population study[END_REF] but not in all. 6;7;20;21 In our adjusted analysis the risk of ASD from assisted conception was no longer statistically significant when controlling for maternal age (Table 3). Thus the increased risk of ASD in assisted conception seems to be partly explained by high maternal age.

There is also a higher risk of ASD in firstborn children in some studies [START_REF] Croen | Descriptive epidemiology of autism in a California population: who is at risk?[END_REF][START_REF] Durkin | Advanced parental age and the risk of autism spectrum disorder[END_REF][START_REF] Glasson | Perinatal factors and the development of autism: a population study[END_REF] but not in others. 6;20;21 A recent meta-analysis find an increased risk in both first born and later born children (>=third). [START_REF] Gardener | Prenatal risk factors for autism: comprehensive metaanalysis[END_REF] In our analyses the risk of ASD from assisted conception did not remain statistically significant when we adjusted for parity, suggesting that the increased risk of ASD in the crude analyses is also partly explained by a large proportion of primipara mothers having assisted conception.

In assisted conception more children are born with low BW and in general the risk of ASD increases with decreasing BW in most studies, 3;6;20;21 however not in one smaller study by Glasson using mean BW.

5

In our study low BW did not seem to explain the increased risk of ASD in assisted conception. This is in contrast to studies of cerebral palsy where the increased risk of cerebral palsy in children born after IVF is partly explained by multiplicity and PTD, factors leading to a high proportion of low BW children 31;40 .

Other studies

Our results contradict previous studies finding a higher occurrence of ASD in children born after assisted conception. 23;24;26;28;29 One case-control study showed a protective association between a general notation of assisted conception on the birth record and infantile autism [START_REF] Maimburg | Do children born after assisted conception have less risk of developing infantile autism?[END_REF] The possibility of biased exposure ascertainment cannot be ruled out in the latter case-control study.

Type of assisted conception

There was a tendency of higher risk estimates in the OI group than in the IVF group, but the differences in risk between the two types of treatment were not statistically significant. However, this trend was consistent in both boys and girls with ASD and might be explained by competing risk factors in the IVF group, as this group is at higher risk for ASD a priori due to higher maternal age, more multiple pregnancies and more primipara mothers (Table 1). However, the risk of ASD was not statistically significant in the adjusted analyses in either type of assisted conception.

Hormonal exposure

In adjusted analyses an increased risk of ASD from FSH exposure was seen, HRR 1.44 (95% CI 1.16-1.80), divided into sex the risk was statistically significant in both boys and girls, but divided into specific treatments the risk was only statistically significant in IVF. Hormonal disorders could link assisted conception with ASD as some underlying causes of infertility are hormone related and hormonal medications are used as part of infertility treatments. Further, various theories for associations between abnormal levels of sex hormones and ASD have been presented based on reports of elevated levels of fetal testosterone found in children with ASD, [START_REF] Auyeung | Fetal testosterone and autistic traits[END_REF] women with ASD were found to have statistically significantly more Polycystic Ovary Syndrome, delayed puberty and irregular menstrual cycles in adulthood than controls 11 and children with ASD were found to have statistically significantly lower levels of serum follicle stimulating hormone and statistically significantly higher levels of serum total testosterone relative to normal reference ranges. [START_REF] Geier | A clinical and laboratory evaluation of methionine cycletranssulfuration and androgen pathway markers in children with autistic disorders[END_REF] Our findings concerning the risk of ASD from specific hormone exposure in assisted conception should be interpreted with caution. In treatment regimes in assisted conception hormones are used in various combinations and hormone exposure groups are thus overlapping; we did not have data to assess specific medication dosages or protocols; we were able to assess FSH use in all OI treatments but only in 51% of the IVF treatments as we could not confirm the specific medications used through the DDPR for the remaining IVF treatments we could not assess medication use in the context of the underlying fertility condition being treated. Accordingly, it is unclear whether it is factors linked to use of FSH (underlying cause of infertility or other conditions), a teratogenic effect from FSH medication per se, the combined effect of exogenous FSH on the endogenous hormonal profile or some perhaps some yet unmeasured factor, underlying the association between FSH medications and ASD. The risk of ASD in different hormone regimes would best be assessed in a randomized controlled trial but this possibility is ruled out by the infrequency of ASD. Consequently, observational studies like this are the only alternative and our findings indicate the need for further, more specific investigations of possible associations between exposure to exogenous FSH and ASD.

Strengths and limitations of the study

Our sources of data were Danish national health registers. These registers are maintained for administrative or monitoring purposes and will therefore not always include specific details desirable for research. For an exposure to IVF we used the IVF Register and according to the Danish legislation, it is mandatory for all public and private fertility clinics in Denmark to report all initiated IVF and ICSI treatments, successful or not, to the IVF Register and the register is therefore considered close to complete. [START_REF] Andersen | The Danish in vitro fertilisation (IVF) register[END_REF] For an exposure to OI we used the DDPR with data on all dispatched prescriptions for hormones used in OI. The main purpose of the DDPR is entirely administrative, that is to ensure that the prescription is not used twice and therefore it is expected that all dispatches are reported to the DDPR. However, it was not possible to establish the date the woman actually administered the medicine. Hormones used in OI can be prescribed for the next 3 cycles and consequently, we had to select a time-window of prescriptions within 3 months before LMP to define the use of OI in the index pregnancy. Therefore the hormones could have been administered 1 or 2 months earlier than the index pregnancy. Additionally we cannot be sure that the woman ever administered the medicine she purchased and therefore the findings regarding OI have to be interpreted with caution.

However, we have no reason to believe such exposure misclassification would be related to ASD; thus, to the extent that we erroneously included some NC children in the OI category, our results might actually be attenuated. The quality of autism diagnoses found in the DPCR has been validated by experts [START_REF] Lauritsen | Quality of Childhood Autism in the Danish Psychiatric Central Register Findings From a Cohort Sample Born 1990-1999[END_REF] ; after evaluating close to 500 medical records of children registered with infantile autism in the DPCR, 94% of the children met the criteria for a correct diagnosis.

As a number of children were followed for less than 6 years some children with ASD may not have been diagnosed before the end of follow up. Also, since the number and proportion of assisted conceptions increased over the study period, a disproportionate number of children born after assisted conception had a shorter follow up time than NC children. To deal with these aspects of the data we used Cox regression models in the statistical analysis which account for differences in follow-up time.

Conclusion

In conclusion, we found no increased risk of ASD in children born after assisted conception overall.

However, in sub analyses we did find an increased risk in a subgroup of women who had OI treatment and were exposed to FSH. Additional studies with more detailed information on underlying causes of infertility and use of specific hormonal medications may help to pinpoint the underlying mechanism of this increased risk.

  e. limiting analysis to infant survivors). Stata 8.2 was used for the analyses, StataCorp. 2003. Stata Statistical Software: Release 8. College Station, TX: StataCorp LP.

Table 1 . Characteristics of women who used assisted conception and their children. Denmark 1995-2003. Numbers and percentages. Natural conception compared to the combined assisted conception group and IVF compared to OI

 1

	Natural	Assisted
	conception	conception	P	IVF	OI	P
	Maternal age		<0.001			<0.001

Through May 8 2008, 3,619 (0.61%) children from the cohort received an ASD diagnosis; an infantile autism diagnosis was given to 1,385 children (0.24%). For all diagnoses the follow-up time was 4 to 13 years with a median follow-up time of 9 years. Within the time period (1995-2003), 3,479 mothers had one child with an ASD diagnosis, 64 mothers had two children with ASD diagnoses and 4 mothers had 3 children with ASD diagnoses. Mothers of children with ASD differed with statistical significance from mothers of other children according to select characteristics; they were older (P 0.002), more often primipara (P <0.001), smokers (P <0.001), they had higher or lower educational level (P <0.001), more preterm deliveries (P <0.001), and children with low BW (P <0.001) (Table

2

).

Table 2 . Characteristics of children with an ASD diagnosis and their mothers, Denmark 1995-2003. Numbers (percentages) Children without ASD compared to children with ASD

 2 In children born after assisted conception the risk of getting an ASD or an infantile autism diagnosis was increased, crude HRR 1.25 (95% CI 1.09-1.43) and crude HRR 1.25 (95% CI 1.01-1.54), respectively.Assessing the risk of an ASD diagnosis divided into specific treatment types IVF or OI, we found a crude HRR of 1.18 (0.95-1.44) and 1.31 (95% CI 1.10-1.56), respectively. In analyses adjusting for maternal age,

	no ASD	ASD	P

Table 3 . Hazard rate ratios and 95% confidence intervals for the association between ASD and assisted conception Crude and adjusted*

 3

		All		Boys		Girls
		Crude	Adjusted**	Crude	Adjusted**	Crude	Adjusted**
	Assisted
	conception	1.25 (1.09-1.43)	1.13 (0.97-1.31)	1.18 (1.01-1.37)	1.07 (0.91-1.27)	1.55 (1.14-2.10)	1.32 (0.93-1.88)
	IVF	1.18 (0.96-1.44)	1.04 (0.83-1.31)	1.15 (0.92-1.44)	1.02 (0.79-1.32)	1.23 (0.75-2.03)	1.02 (0.57-1.81)
	OI	1.31 (1.10-1.56)	1.20 (0.99-1.44)	1.20 (0.99-1.47)	1.12 (0.91-1.38)	1.80 (1.24-2.61)	1.56 (1.03-2.36)
	* Reference = natural conception
	** Adjusted for maternal age, educational level, parity, smoking, BW and multiplicity

Table 4 . Crude hazard rate ratios and 95% confidence intervals for the association between ASD and IVF according to specific underlying cause of infertility. Percentages within categories. ASD

 4

		Numbers	Numbers
	Underlying factor	exposed	exposed	Crude HRR
	Natural conception	555,828	3.394 (0.61%)	ref.
	Mixed or unspecific factors	5,136	39 (0.76%)	1.49 (1.08-2.04)
	Tubal factor	4,859	26 (0.54%)	0.86 (0.59-1.27)
	Ovulation factor	929	7 (0.75%)	1.63 (0.78-3.42)
	Male factor	4,019	23 (0.57%)	1.18 (0.79-1.78)

Table 5 . Hazard rate ratios and 95% confidence intervals for the association between ASD and assisted conception combined and divided into IVF and OI according to specific hormones*

 5

	Assisted conception,	Assisted conception,	Assisted conception,
		all	boys	girls	IVF, all	OI, all
	Downregulation	0.82 (0.49-1.37)	0.79 (0.45-1.40)	0.93 (0.29-2.92)	0.57 (0.28-1.16)	1.09 (0.48-2.51)
	FSH	1.44 (1.16-1.80)	1.32 (1.02-1.70)	1.99 (1.23-3.18)	1.73 (1.10-2.71)	1.29 (0.89-1.89)
	hCG	1.28 (1.05-1.55)	1.16 (0.93-1.44)	1.85 (1.23-2.77)	1.37 (0.87-2.15)	1.17 (0.79-1.71)
	Clomiphene	1.14 (0.93-1.40)	1.02 (0.80-1.29)	1.75 (1.15-2.65)	1.34 (0.54-3.32)	0.82 (0.53-1.28)
	* Reference = natural conception, adjusted for maternal age, education, parity, smoking, BW and multiplicity
	** Data from the Danish Drug Prescription Register (DDPR) on specific medications used during IVF treatment available for all OI women and
	51% of IVF women

Funding: the study was funded as a co-financed PhD project by The Danish Agency for Science, Technology and Innovation, University of Aarhus and The Elsass Foundation. Further funding was applied by Sofiefonden, The Health Insurance Foundation, The Augustinus Foundation, Julie von Müllens Foundation, Direktør Jacob Madsen & Hustru Olga Madsens Fond and Aase and Ejnar Danielsen Foundation.

Competing interests: all authors declare that the answer to the questions on your competing interest form are all No and therefore have nothing to declare.

Contributions: DH, PT and JG were responsible for conception and design. DH, JG and CS conducted the statistical analyses and the interpretation of data. DH wrote the first draft of the manuscript. All authors provided critical input at all stages and critically reviewed and contributed to the final draft. PT and DH are guarantors of the study.

Ethical approval: approval for this study was obtained from the Danish Data Protection Agency.

Access statement: all authors had full access to the data.

Exclusive licence: the Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in BMJ editions and any other BMJPGL products.


Elizabeth Webb
email: elizabeth.a.webb@imperial.ac.uk

David Blane

Anne Mcmunn

Gopalakrishnan Netuveli

PROXIMAL PREDICTORS OF CHANGE IN QUALITY OF LIFE AT OLDER AGES IN OLDER AGE

Keywords:

Objective

To investigate whether changes in health, social and economic circumstances predict change changes in quality of life at older ages. Design Secondary analysis, using multiple regression analyses, of waves 1 and 3 of the English Longitudinal Study of Ageing. England, 2002-2006 Participants Nationally representative sample of non-institutionalised adults aged over 50 living in England with non-missing data for the variables of interest (N=4848).

Setting

Main outcome measure Quality of life (QoL) in 2006, with reference to QoL in 2002, both measured using CASP-19 score.

Results

Mean QoL decreased over the four years, with becoming depressed (β -0.12) and developing difficulties with activities of daily living (β -0.08) contributing to this decrease. Improvements in family relationships (β 0.10), the neighbourhood (β 0.08) and perceived financial position (β 0.07) each counteracted the decline in QoL. Initial QoL strongly predicted QoL at follow-up (β 0.46). Regression models explained 59% of variation in QoL, and there were small differences between age and gender groups.

Conclusions

Maintenance of good QoL in older aged people is enabled by the avoidance of depression, maintenance of physical function, good neighbourhood standards, family relationships and financial circumstances.

INTRODUCTION

In an era of ageing populations, successful ageing through the maintenance of health and quality of life (QoL) is of primary economic and social importance. Age has a substantial influence on QoL and related measures although the age at which QoL peaks is disputed: two US studies, the first a longitudinal study of nearly 2000 male veterans [START_REF] Mroczek | Change in life satisfaction during adulthood: findings from the Veterans Affairs Normative Aging study[END_REF] and the second the United States General Social Survey 2 found, respectively, that life satisfaction peaked at 65 years and happiness peaked at 50 years. Cross-sectional work using the English Longitudinal Study of Ageing (ELSA) has shown amongst a population of over-50s that average QoL score increased to 68 years and declined thereafter. [START_REF] Netuveli | Quality of life at older ages: evidence from the English Longitudinal Study of Ageing (wave 1)[END_REF] There is also a body of evidence (e.g. 4;5 ) which suggests that the curvilinear relationship between age and wellbeing is U-shaped, with a minimum during the late 40s. However, age does not determine QoL in older ages 3;6 and, particularly in this time of increasing duration of good health following labour market exit, increasing age need not be greeted with the assumption of declining QoL. Current marital status, 3;7 social contact and relationships, 3;6 socioeconomic circumstances 3;8 and health, 3;6;9 all influence QoL at older ages, and early life also has an influence which acts through current circumstances, for example property ownership, or life course influences on health. [START_REF] Blane | Life course influences on quality of life in early old age[END_REF] It has been suggested that individual's QoL is relatively stable over time, even in older age; [START_REF] Grundy | Enhancing the quality of extended life years. Identification of the oldest old with a very good and very poor quality of life[END_REF] that although major events affect it, QoL quickly begins to return to the earlier level, for instance in the case of onset of disability. [START_REF] Oswald | Does happiness adapt? A longitudinal study of disability with implications for economists and judges[END_REF] Perceived control, a dimension of QoL, has been shown to have weekly variation amongst older people, but that intra-individual magnitude of this fluctuation is stable over time. [START_REF] Eizenman | Intraindividual variability in perceived control in a older sample: the MacArthur successful ageing studies[END_REF] What has not previously been investigated is the manner in which common changes in circumstances influence the way in which QoL changes over time in middle and older age. The aim of this longitudinal study is, therefore, to investigate whether change changes in QoL over time in older aged people over the age of 50 are explained by changes in: i) economic circumstances; ii) social contact and relationships and iii) health. We hypothesise that negative changes in each of these dimensions of life will be associated with decreases in QoL over time, and conversely that improvements in circumstances will be linked to increased QoL.

METHODS

Data

We have used data from the first and third waves of the English Longitudinal Study of Ageing (ELSA), which were collected in 2002 and 2006, respectively. ELSA is a cohort study of a nationally representative sample of non-institutionalised adults who were aged 50 years or older and living in England in 2002. Wave one had a response rate of 67% and wave three achieved, from those who remained eligible and who responded at wave one, a response rate of 71%. [START_REF]Seminar on attrition and non-response in longitudinal studies of ageing[END_REF] Between waves one and three there was 9.4% mortality amongst the total ELSA sample. 15 At wave three, 7535 of the original 11 234 core members of ELSA remained in the study. Analyses here are based on core ELSA members with no item missingness for the variables of interest, which, in the complete model, results in a sample size of 4848 individuals.

Outcome

The outcome variable we have used here to indicate change in QoL is CASP-19 score at wave 3 of ELSA, with reference to CASP-19 score at wave 1. QoL measures two waves apart were used although CASP-19 was collected at all waves of ELSA, as the time elapsed between each consecutive wave was too short for sufficient events and changes to have occurred in participant's lives. CASP-19 is a measure of QoL in older age designed to reflect the features of contemporary society which influence early old age whilst being independent of factors, including financial circumstances and health, which may influence it. CASP-19 contains 19 Likert-scaled items, which each access one of four domains: Control; Autonomy; Self-realisation and Pleasure. Each item is numerically coded, such that the most positive responses are attributed a score of 3 and the most negative, a score of 0. The possible range of CASP-19 scores is 0 to 57, with higher scores indicating a better QoL.

The theoretical background, development and technical properties of CASP-19 are described fully elsewhere. 16;17 The reliability and validity of CASP-19 as a measure of QoL have also been explored in detail elsewhere; [START_REF] Hyde | A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19)[END_REF][START_REF] Wiggins | Quality of life in the third age: key predictors of the CASP-19 measure[END_REF][START_REF] Wiggins | The Evaluation of a Self-enumerated Scale of Quality of Life (CASP-19) in the Context of Research on Ageing: A Combination of Exploratory and Confirmatory Approaches[END_REF] each of the four CASP domains is internally consistent with Cronbach's Alpha scores of 0.6 to 0.8 and CASP correlates with the Life Satisfaction Index -well-being scale [START_REF] James | The life satisfaction index -well-being: its internal reliability and factorial composition[END_REF] (r=0.63). CASP-19 contains 19 Likert-scaled items, which each access one of four domains: Control; Autonomy; Self-realisation and Pleasure. Each item is numerically coded, such that the most positive responses are attributed a score of 3 and the most negative, a score of 0. The possible range of CASP-19 scores is 0 to 57, with higher scores indicating a better QoL.

Explanatory variables

All multivariate models included age, age squared (to account for the non-linearity of the relationship between QoL and age), gender and CASP-19 score at baseline (ELSA wave 1). Age squared was included as a covariate as the relationship between QoL and age is non-linear; with increasing age beyond the age of 50 years, QoL has been shown to change in a quadratic manner. [START_REF] Netuveli | Quality of life at older ages: evidence from the English Longitudinal Study of Ageing (wave 1)[END_REF] Economic circumstances, social contact and relationships and health indicators were all considered both in terms of baseline levels and change over the follow-up period. The majority of these indicators have been previously shown to predict CASP-19 scores cross-sectionally. [START_REF] Netuveli | Quality of life at older ages: evidence from the English Longitudinal Study of Ageing (wave 1)[END_REF] Indicators of economic circumstances were: i) change in wealth over the follow-up period; ii) wealth at baseline; iii) change in income; iv) income at baseline; v) change in perceived financial position, categorised as up, down or stable and vi) perceived financial position at baseline, dichotomised into those who reported that they managed well or quite well, and those who gave more negative responses.

Health indicators included binary variables indicating the presence of long-standing illness (LSI), limiting long-standing illness (LLSI) and depression. This latter variable was derived from the eight item Centre for Epidemiologic Studies Depression (CES-D) scale, from which a score of 3 or more indicated depression. Changes in these binary indicators of health over the follow-up period were defined as improving, static or worsening, with the static group being treated as the baseline. Physical functioning was measured using thirteen activities of daily living (ADL): each activity with which an individual had difficulties was attributed one point and the change in physical functioning defined as the difference between this score at baseline and the end of follow-up.

Indicators of social contact and relationships were: whether relationships with i) family and ii) friends were based on understanding, support and confidence; the frequency of contact with iii) family and iv) friends and v) the number of persons with whom participants felt they had close relationships. The five variables were derived from multiple items in the survey, which had multiple ordinal responses. Summary scores were created by summing item responses after ensuring a uniform direction of response with more positive responses being attributed higher scores. Changes in each of these variables were defined as the difference in the score between the waves. Further indicators were changes in marital status, including getting married, separating or divorcing, or being widowed during the follow-up period and marital status at wave one, categorised into married, single, separated or divorced, or widowed.

A summary score indicating the characteristics of the neighbourhood in which participants lived was based on nine items, including the presence of vandalism, a sense of belonging, trust and support. These items were each scored with values from 0 to 6 and they were summed to give a single score ranging from 0 to 54, a higher score indicating a better neighbourhood with more positive characteristics.

Analysis

Baseline characteristics of those included in analyses and excluded due to missing data were compared using chi-squared and student's t-tests. Preliminary analyses calculated the mean CASP-19 score at baseline and mean changes in CASP-19 scores over the follow-up period, by age, gender, income, baseline CASP-19 score and LSI. Student's t-tests tested differences between groups. Those whose CASP-19 score increased were compared in terms of all covariates of interest using chisquared and student's t-tests to those whose score decreased.

Conditional change multiple linear regression models were fitted. The dependent variable was CASP-19 score at wave 3 and CASP-19 score at wave one was included as an independent variable, to account for the lack of independence between QoL scores. Models included: i) age, age squared and gender; ii) as previous, plus health, economic and social measures at baseline; iii) as previous, plus change in health, economic and social measures over the follow-up period. To further investigate the effects of age and gender the analysis was repeated separately for three baseline age groups (50-64, 65-74, 75+) and for men and women.

All analyses were conducted using Stata SE version 10. 1 2 7

RESULTS

Those excluded from the analyses, either due to point missingness or absence from wave 3 of ELSA, were older, had lower incomes and baseline QoL, and were more likely to have a LLSI (table 1). Those whose QoL improved over the follow-up period were younger than those whose QoL decreased, but there were not differences in gender (data not shown). They did not differ in terms of their social contact and relationships or the quality of their neighbourhoods, but in many regards they were at a disadvantage at baseline: initial QoL was lower and, although they had the same objective financial situation, they had a worse perceived financial situation and health at baseline. Over the follow-up period, however, those whose QoL improved experienced more positive changes: a smaller decrease in income and perceived financial situation, health, social contact and relationships and neighbourhoods which were more likely to have improved.

In the first multivariate regression model, 49% of the variation in CASP-19 score at the end of followup was explained (table 3). Age and age squared were both associated with CASP-19 score at the end of follow-up, and gender did not have an influence. If other covariates could be held constant, CASP-19 score would fall by around 30% over the follow-up period. Improving perceived financial situation and increasing income were linked to increased CASP-19 scores at the end of follow-up, whilst worsening perceived financial situation was linked to decreased CASP-19. Changes in wealth did not influence changes in CASP-19, and each additional £10,000 income was associated with 0.1 additional CASP units.

Improving health was linked to higher CASP-19 scores at the end of follow-up, although the negative effects of worsening health were more substantial for both LSI and LLSI. Each additional difficulty with an ADL was linked to a decrease of 0.5 CASP units, whilst becoming depressed was associated with a decrease of 3.5 CASP units. After age and CASP-19 score at wave 1, becoming depressed or recovering from depression were the strongest predictors of final CASP-19 score.

Improving quality of relationships and neighbourhood, increasing frequency of contact with friends, increasing numbers of close relationships and being widowed during the period of follow-up were all linked to a higher final CASP-19 score. Increasing frequency of contact with family members was linked to a lower CASP-19 score. When the final model was re-run for three age groups (50-64, 65-74, 75+) overall effects were similar, with an indication that economic improvements may have a smaller positive effect in the oldest age group, and when the same model was run separately for men and women there were no 1 apparent gender effects (data not shown). 2 DISCUSSION Economic, health and social changes are all linked to change changes in individual's QoL over time. The factors which influence change in QoL relate closely to those which have a cross-sectional relationship with QoL 3 and, as there is no sole predictor of QoL, there is no single predictor of change in QoL the way in which QoL changes.

Those factors which have the greatest influence upon change in QoL are age and initial QoL. After these, depression, physical functioning, perceived financial position, quality of family relationships and the quality of the neighbourhood are the strongest influences. All these associations operate in the expected direction, such that a worsening of circumstances is linked to a reduction in QoL. This research confirms previous findings from this and other study populations that strong personal relationships, good health and financial security are important in maintaining a good QoL in older age. 3;6-9 This research is based upon data from a representative sample of the English population aged over 50 years, but the interpretation of the results outlined here may be limited due to the proportion of missing data. Analyses are based on the 4848 individuals with complete data on all the variables of interest, who remain from the 11 234 participants in the first round of ELSA. The majority of this missingness is a result of 35% of participants who were not present at wave three (for full details of the ELSA response rates, see 14;15 ) and an additional 17% of original participants who had one or both measures of CASP-19 missing.

Those included in the analyses and excluded due to missing data differ in terms of age, income, prevalence of LLSI and QoL. These differences, however, do not lead to questions regarding the internal validity of the results, and there is no reason to suspect that the relationship between changes in economic, social and health circumstances and change in QoL would differ in the excluded group. Those participants who remain in the study at the end of follow-up are more advantaged at baseline than those who do not (table 1), providing further evidence for the widely recognised effect that retention in cohort studies is higher amongst those who are more advantaged. [START_REF] Purdie | Health and demographic characteristics of respondents in an Australian national sexuality survey: comparison with population norms[END_REF][START_REF] Sonne-Holm | Influence of fatness, intelligence, education and sociodemographic factors on response rate in a health survey[END_REF][START_REF] Goldberg | Socioeconomic, Demographic, Occupational, and Health Factors Associated with Participation in a Long-term Epidemiologic Survey: A Prospective Study of the French GAZEL Cohort and Its Target Population[END_REF] The positive correlation between QoL at wave one and QoL at wave three (correlation coefficient=0.68) provides evidence for continuity in QoL, despite changes in circumstances leading to changes in QoL. In their study of the effect of incident disability on happiness, Oswald and Powdthavee showed that happiness levels began to recover within a few years. [START_REF] Oswald | Does happiness adapt? A longitudinal study of disability with implications for economists and judges[END_REF] In order to explore the capacity of individuals to fully recover their previous QoL following a negative change in circumstances, a longer term study is required. Despite the correlation in individuals' QoL scores over time, there is a trend towards worsening QoL over time at all ages in this middle and older aged population. For example, the CASP-19 score for a 60 year old at the end of follow-up (wave 3, 2006) is a mean of 2.5 CASP units lower than for a participant of the same age at the beginning of the study (wave 1, 2002). CASP-19 scores at wave 2 of ELSA (2004) were very similar to those at wave 1, so it is unlikely that the depreciation observed at wave 3 indicates a real fall in QoL. A more likely explanation is that the difference is an artefact; in the wave 3 ELSA questionnaire the QoL section was positioned immediately after the General Health Questionnaire (GHQ), which is designed to detect psychiatric illness and may have left participants feeling negative, whilst in the first and second waves it was positioned after more neutral questions about education and qualifications, and about social activities. This potential methodological issue does not, however, invalidate the results reported here. When analyses were repeated investigating the change in QoL between waves one and two of ELSA, rather than waves one and three, the results were very similar. This issue does, however, merit further investigation which is unfortunately outside the scope of this paper.

We have shown in this work that those dimensions of social life, including the quality of relationships with family and friends, the number of close relationships and the frequency of contact with friends, which have a positive influence on QoL cross-sectionally 3 also have a positive influence on change in QoLthe way in which QoL changes over time, and that changes in these factors also have an influence. Good quality relationships contribute positively to an individual's psychological wellbeing, [START_REF] Umberson | The effect of social relationships on psychological well-being: Are men and women really so different?[END_REF][START_REF] Bowling | Social Networks, Health, and Emotional Well-being Among the Oldest Old in London[END_REF][START_REF] Pinquart | Influences of socioeconomic status, social network, and competence on subjective well-being in later life: a meta-analysis[END_REF] a psychological concept which is a dimension of, 27;28 and correlates highly with, [START_REF] Hyde | A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19)[END_REF] QoL. Strong social networks may also delay functional decline, [START_REF] Unger | Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies on Successful Ageing[END_REF] encouraging maintenance of good QoL. which is closely linked to how people evaluate their QoL. The apparent negative influence of frequent contact with family members is in contrast to the positive influence of contact with friends possibly indicating that this contact is related to caring for, or being cared for by, relatives. This contact may be linked to a sense of duty in the former case, or poor health in the latter, and a loss of independence in both situations, therefore explaining the negative impact upon QoL. The negative impact on QoL of caring for dependent elderly relatives is well documented. 30;31 The positive change in QoL relating to being widowed before or during the follow-up period is unlikely to indicate that being widowed is a positive experience. It may be an indication that these individuals had a particularly low initial QoL, due to recently having been widowed or to caring for a partner in poor health, and QoL improved over the follow-up period as bereaved participants began to recover from grief. The lack of apparent further influence of marital status on QoL may be because the necessary level of detail about individual's circumstances is not available here; the influence of marriage or divorce will depend upon the circumstances of the relationship before the event. It has previously been shown that marriage improves happiness [START_REF] Stack | Marital Status and Happiness: A 17-Nation Study[END_REF] and well-being [START_REF] Coombs | Marital Status and Personal Well-Being: A Literature Review[END_REF] but also that the quality of a marriage, rather than marriage itself, is the important influence upon wellbeing. [START_REF] Gove | Does Marriage Have Positive Effects on the Psychological Well-Being of the Individual[END_REF] Individuals with poor and worsening health and poor and worsening financial circumstances have greater decreases in QoL, however we might note that those health and economic measures which have the greatest influence are those which are subjective or psychologically influenced, rather than objective. For example perceived financial circumstances have a greater influence than either income or wealth, and depression greater than LSI or LLSI. These stronger relationships mirror previous findings, [START_REF] Bowling | Towards the Good Life: A Population Survey of Dimensions of Quality of Life[END_REF] and reinforce QoL as a subjective measure, which tends to correlate with subjective measures of health and social and economic circumstances.

What was previously known Cross-sectionally, social, economic and health factors are associated with QoL in older age.

What this study adds QoL declines over time in older aged people, and common changes in circumstances affect the rate of this decline. Improved perception of financial circumstances and quality of neighbourhoods can decelerate decline in QoL, as can improving relationships with family members. Becoming depressed and developing difficulties with physical functioning both accelerate decline in QoL.

QoL is declining in older aged people, but potential measures to avoid or reduce this decline include improving the financial situation of older people, improving the neighbourhoods in which older people live and a greater recognition of and improved facilities for the treatment of depression in older people. This research was funded by ESRC Grant number RES-596-28-0001.

Competing Interest: None declared.

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd to permit this article (if accepted) to be published in JECH and any other BMJPGL products and sublicences such use and exploit all subsidiary rights, as set out in our licence (http://jech.bmj.com/ifora/licence.pdf).

Table 1 .

 1 Comparison of ELSA participants included and excluded from analyses, with p-values indicating results of chi-squared and student's t-tests of the differences between groups.

		I n c l u d e d	Excluded due to point	Excluded as not present in
		(N=4848)	missingness (N=2456)	Wave 3 (N=3973)
		Mean [SD] or %	Mean [SD] or %	p	Mean [SD] or %	p
	Age (years)	63.1 [9.1]	65.3 [10.1]	<0.001	67.6 [11.4]	<0.001
	Female gender	54.8	56.6	0.133	52.7	0.051
	Income (£10 000s)	1.56 [2.03]	1.11 [2.14]	<0.001	0.58 [1.29]	<0.001
	CASP	43.4 [8.2]	42.3 [8.4]	<0.001	41.1 [9.4]	<0.001
	LLSI	29.9	35.3	<0.001	41.2	<0.001
	†in £10 000s

CASP-19 score (range 0-57, baseline median 45) decreased by a mean of 2.2 CASP units over the follow-up period (table

2

). This decrease in QoL did not differ according to gender, but was greater in older age groups; those aged 75 and over experienced a decrease 2.5 times greater than those younger than 65 (-3.8 CASP units compared to -1.5) (p<0.001). QoL decreased most amongst: people of intermediate income at wave 1 (p=0.003); people with median and above initial CASP-19 scores (p<0.001); people without a LLSI at wave 1 (p<0.001).

Table 2 .

 2 Description of the variables used in the study. Unadjusted baseline CASP-19 scores and change in CASP-19 score (mean [SD 95%CI]), according to sociodemographic variables in the 5434 participants with complete information on CASP-19 at waves 1 and 3 of ELSA.

	Variable	Number	Wave 1 CASP score	Change in CASP score
	All	4848	43.4 [8.2 43.2, 43.6]	-2.2 [6.6 -2.4, -2.0]
	Age (years)
	5 0 -6 4	2835	43.5 [8.4 43.2, 43.8]	-1.5 [6.6 -1.8, -1.3]
	6 5 -7 4	1388	43.9 [7.8 43.5, 44.3]	-2.9 [6.3 -3.2, -2.6]
	75+	625	41.9 [7.9 41.3, 42.6]	-3.8 [6.9 -4.3, -3.3]
	Gender
	Female	2656	43.4 [8.0 43.1, 43.7]	-2.2 [6.5 -2.5, -1.9]
	Male	2192	43.4 [8.4 43.0, 43.7]	-2.2 [6.7 -2.5, -2.0]
	Wave 1 income tertiles
	Lowest	817	41.0 [9.3 40.3, 41.7]	-2.1 [7.2 -2.6, -1.7]
	Intermediate	1576	42.6 [8.1 42.1, 43.0]	-2.6 [6.7 -2.9, -2.2]
	Highest	2455	44.7 [7.2 44.4, 45.0]	-2.0 [6.2 -2.2, -1.7]
	Wave 1 CASP score
	≤44	2304	36.6 [6.5 36.3, 36.9]	-0.37 [7.1 -0.66, -0.08]
	≥45	2544	49.6 [3.1 49.4, 49.7]	-3.87 [5.6 -4.09, -3.66]
	Wave 1 Long-standing illness
	No	2264	45.9 [7.0 45.6, 46.1]	-2.43 [6.4 -2.69, -2.17]
	Non-limiting	1135	45.0 [6.8 44.6, 45.4]	-2.44 [6.1 -2.79, -2.08]
	Limiting	1449	38.3 [8.7 37.9, 38.8]	-1.68 [7.3 -2.05, -1.30]

Table 3 .

 3 Conditional change linear regression model, comparing CASP-19 score at wave 3 to wave 1. Included covariates were age, age squared, gender and CASP-19 at baseline.The second multivariate regression included covariates at baseline and explained 51% of the variance in CASP-19 score at the end of follow-up (table4). Main influences were age, age squared and CASP-19 score at baseline, with additional positive influences of economics (wealth, income, perceived financial situation) and aspects of social contact and relationships (quality of family relationships, being widowed, rating of the neighbourhood), and negative influences of poor health (depression, LLSI) and contact with family.

	N=4848	B coeff	9 5 % C I		p	S t a n d a r d i s e d B
	Age (years)	0.27	0.03, 0.52		0.029	0.29
		-3		-3	-3
	Age squared	-2.88 x 10	-4.76 x 10	, -1.01 x 10	0.003	-0.40
	Female gender	0.01	-0.34, 0.35		0.970	0.00
	Wave 1 CASP score	0.71	0.69, 0.73		<0.001	0.68
	Constant	18.50	13.91, 23.10		-	-
	2
	r	0 . 4 9		-		-	-

Table 5 .

 5 Multivariate linear regression (conditional change model) of CASP-19 score at wave 3 compared to wave 1. All effects shown are of the change in covariates over the follow-up period.Baseline covariates are also included in the model but these effects are not shown here.

	N=4848			B coeff	95% CI		p	Standardised B
	Demographics
	Age (years)			0.38	0.15, 0.61		0.001	0.40
				-3	-3
	Age squared			-3.59 x 10	-5 . 3 5 x 1 0	,	<0.001	-0.50
					-3
					-1.83 x 10
	Female gender			0.19	-0.14, 0.52	0.264	0.01
	Wave 1 CASP score			0.48	0.45, 0.51		<0.001	0.46
	Economic circumstances
	Wealth (£10 000s)			0.00	0.00, 0.01		0.095	0.02
	Income (£10 000s)			0.10	0.01, 0.20		0.031	0.02
	Perceived financial position	Improving	1.74	1.17, 2.30		<0.001	0.07
			Worsening	-1.45	-1.99, -0.91	<0.001	-0.05
	Health
	ADL			-0.54	-0.69, -0.40	<0.001	-0.08
	Depression		Improving	3.44	2.74, 4.15		<0.001	0.12
			Worsening	-3.46	-4.02, -2.90	<0.001	-0.12
	LSI		Improving	0.28	-0.30, 0.87	0.339	0.01
			Worsening	-1.00	-1.54, -0.45	<0.001	-0.04
	LLSI		Improving	0.81	0.08, 1.53		0.030	0.03
			Worsening	-1.59	-2.16, -1.01	<0.001	-0.06
	Social contact and relationships
	Contact with friends			0.14	0.06, 0.21		<0.001	0.05
	Contact with family			-0.08	-0.13, -0.04	<0.001	-0.05
	Quality of friendships			0.10	0.05, 0.15		<0.001	0.05
	Quality of family relationships		0.11	0.08, 0.15		<0.001	0.11
	Number of close relationships		0.03	0.01, 0.05		0.004	0.03
	Changes in marital	Married		0.72	-0.06, 1.50	0.069	0.02
	status	Separated / divorced	-0.28	-1.44, 0.88	0.637	0.00
		Widowed	0.86	0.09, 1.64		0.029	0.02
	Neighbourhood		Improving	1.86	1.38, 2.35		<0.001	0.08
			Worsening	-1.41	-1.92, -0.90	<0.001	-0.05
	Constant			22.59	18.19, 26.98	<0.001	-
	2
	r			0 . 5 9	-		-
	†in £10 000s


Williams Ed

Emily D Williams
email: emily.williams@ucl.ac.uk.

Emmanuel Stamatakis

Tarani Chandola

An assessment of physical activity levels in South Asians in the UK: Findings from the Health Survey for England

Keywords: Physical activity, Ethnic minorities, CHD risk factors, Population health

Background: South Asians in Britain experience high rates of coronary heart disease (CHD) compared with other ethnic groups. Behavioural risk factors, such as physical inactivity, have been explored as possible explanations for this trend. However, there have been few comprehensive accounts describing physical activity levels of this ethnic group.

Methods: Data from the Health Survey for England (1999-2004) on 5421 South Asians and 8974 whites aged 18-55 were used to compare physical activity levels. Analyses of covariance tested the association between ethnicity and self-reported total physical activity metabolic equivalents of task (MET) scores, adjusting for age, sex, self-reported health, adiposity, and socioeconomic status.

Results: Total MET-minutes/week were consistently lower in UK South Asians compared with whites (973 compared with 1465 MET-minutes, p < .001). This ethnic group difference was consistent across sexes, age-and sub-groups, and was independent of covariates. South Asians born in the UK reported higher levels of physical activity than those South Asians born elsewhere (p < .001). Variables such as urbanisation and psychological distress were associated with physical activity; however, despite their inclusion in the models, ethnic group differences remained, indicating that physical inactivity in South Asians was not attributable to area or individual sociodemographic factors.

Conclusions: Physical activity levels are very low in UK South Asians; this is consistent across all examined population subsets. Physical inactivity is likely to contribute to their high risk of CHD. Increasing physical activity in all UK South Asians should be a public health priority for health professionals.

South Asian populations, originating from the Indian subcontinent suffer higher rates of coronary heart disease (CHD) compared with other ethnic groups. [START_REF] Yusuf | Global burden of cardiovascular diseases Part II: Variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention strategies[END_REF] This vulnerability is particularly apparent in migrant South Asians in the UK. [START_REF] Lip | Ethnicity and cardiovascular disease prevention in the United Kingdom: a practical approach to management[END_REF] A number of biological explanations have been proposed as potential contributors to this vulnerability. South Asians are prone to have higher insulin resistance, [START_REF] Cappuccio | Ethnicity and cardiovascular risk: variations in people of African ancestry and South Asian origin[END_REF] central obesity, [START_REF] Barnett | Type 2 diabetes and cardiovascular risk in the UK south Asian community[END_REF] and lower high density lipoprotein cholesterol [START_REF] Falaschetti | Blood analytes In[END_REF] than other groups. Despite this, other conventional biological CHD risk factors, such as hypercholesterolaemia and hypertension, are not consistently elevated in UK South Asians. [START_REF] Karthikeyan | Lipid Profile, Plasma Apolipoproteins, and Risk of a First Myocardial Infarction Among Asians: An Analysis From the INTERHEART Study[END_REF][START_REF] Agyemang | Is the blood pressure of South Asian adults in the UK higher or lower than that in European white adults? A review of cross-sectional data[END_REF] Behavioural explanations have been explored, demonstrating a mixed risk profile. There is marked variation in smoking rates between South Asian sub-groups, with tobacco use particularly high in Pakistani and Bangladeshi men and low in Indian and female groups. [START_REF] Wardle | Use of tobacco products[END_REF] Dietary habits remain insufficiently examined, although there is some evidence to suggest that South Asians eat more fruit and vegetables, and less fat than the general population. [START_REF] Williams | Coronary risk in a British Punjabi population: Comparative profile of non-biochemical factors[END_REF][START_REF] Sevak | Relationship of hyperinsulinemia to dietary intake in south Asian and European men[END_REF] Regular physical activity reduces the risk of CHD and type 2 diabetes, through a range of mechanisms including reduced adiposity, improved endothelial function, improved lipid and glucose profiles, and lowering blood pressure. [START_REF] Warburton | Health benefits of physical activity: the evidence[END_REF] Some studies have examined leisure-time physical activity among South Asians residing in the UK and in South Asia, demonstrating lower levels of activity in South Asians compared with other ethnic groups. [START_REF] Fischbacher | How physically active are South Asians in the United Kingdom? A literature review[END_REF][START_REF] Joshi | Risk factors for early myocardial infarction in South Asians compared with individuals in other countries[END_REF] However, these studies have not consistently accounted for age-group, sex, socioeconomic and country of birth sub-group variations, [START_REF] Mckeigue | Relationship of glucose intolerance and hyperinsulinaemia to body fat pattern in South Asians and Europeans[END_REF][START_REF] Bryan | Physical activity and ethnicity: Evidence from the Canadian Community Health Survey[END_REF] nor have they been performed in nationally-representative populations. [START_REF] Riste | High Prevalence of Type 2 Diabetes in All Ethnic Groups, Including Europeans, in a British Inner City[END_REF][START_REF] Hayes | Patterns of physical activity and relationship with risk markers for cardiovascular disease and diabetes in Indian, Pakistani, Bangladeshi and European adults in a UK population[END_REF] This study aimed to provide a comprehensive examination of leisure-time physical activity levels in South Asians in comparison with UK whites, using nationallyrepresentative data from the Health Survey for England (HSE).

METHODS

The HSE comprises a series of annual surveys that began in 1991. All surveys covered the adult population aged 16 and over living in private households in England. HSE samples are selected using multi-stage stratified probability design to give a representative sample of the target population. Stratification is based on geographical entities and not on individual characteristics: postcode sectors selected at the first stage and household addresses selected at the second stage. Further details on sample design and sample selection can be found elsewhere. [START_REF]Health Survey for England. The health of minority ethnic groups '99[END_REF] The present analyses combined data from the 1999, 2003, and 2004 HSE datasets. The focus of the 1999, 2003, and 2004 surveys was cardiovascular disease and related risk factors for adults. In addition, in 1999 and 2004 minority ethnic groups were over-sampled to boost the sample size of ethnic minorities living in households in England. Participants gave full informed consent to participate and ethical approval was obtained from the North Thames Multi-Centre Research Ethics Committee. The average response rate over the survey years for the general population was 74% and 67% for the ethnic boost samples.

Procedures and measures

Survey interviewers visited eligible households and collected data on demographics (e.g. marital status, country of birth, religion), anthropometry (height, weight, waist and hip circumference), psychological distress, and health behaviours (smoking, alcohol intake, and physical activity). All survey materials were translated into Urdu, Punjabi, Gujarati, Hindi, and Bengali, by an external company using independent proof-reading and stringent verification procedures. Interviewers who spoke and read these languages (as well as English) were recruited and received full training. Ethnicity was categorised by the subject's family origins (only South Asian participants originating from India, Pakistan, and Bangladesh, and white British were included in this study).

Country of birth was used as a proxy acculturation measure. Socioeconomic status (SES) was measured using highest educational qualifications, total equivalised (adjusted for household size) household income, and occupational status. Occupational grade was categorised using the Registrar General social classification system. Access to a car was assessed by one question and an additional two items measured the degree of urbanisation of residential area (urban, suburban, or rural) and availability of local leisure facilities. Psychological distress was assessed from the 12-item version of the General Health Questionnaire (GHQ-12). [START_REF] Goldberg | A scaled version of the General Health Questionnaire[END_REF] The well-established questionnaire enquires about depression and anxiety symptoms, and sleep disturbance over the last month. Measures of current health status were recorded: two questions explored self-reported health and limiting long-standing illnesses.

Detailed information on the general and specific survey method can be found elsewhere. 20 Frequency and duration of leisure-time physical activities in the four weeks prior to the interview (walking and domestic activity: for at least 30 minutes per day; leisuretime sports/exercises: for at least 15 minutes per day ) were assessed across three domains of activity: leisure-time sports/exercises (e.g. cycling, aerobics, and ball sports such as football), domestic activities (heavy housework, heavy 'Do-It-Yourself', gardening, heavy manual), and walking for any purpose. All physical activities were assigned a metabolic equivalent task (MET) value, using the Compendium of Physical Activities. [START_REF] Ainsworth | Comparison of three methods for measuring the time spent in physical activity[END_REF] A MET score of 1 corresponds to the rate of energy expenditure when at rest (1 kcal/kg/h). MET-minutes per week were computed as MET for each specific activity multiplied by the number of minutes the activity was performed per week. METminutes per week were summed, creating a total score (totalMETs); a cut off of 8000 totalMETs (corresponding to approximately over 2 hrs per day of highly vigorous exercise) was set to eliminate potential outliers and participants involved in heavy sports and exercise-training. In addition, a total score was created for MET-minutes expended during sporting activities during the past week (sportMETs). A binary variable was also created to explore the government's recommendations for weekly activity (those who did and did not peform ≥450 MET minutes of moderate-vigorous activities). For this, only sports activities were included, since sports MET scores do not include light activity.

Since the 1999 and 2003-4 versions of the HSE questionnaire had no duration questions on walking and domestic physical activity sessions, we extracted the sex and age (5year bands) mean session duration values from the 1997-98 datasets and assigned the resulting values to each respondent to calculate time in moderate to vigorous physical activity and consequently MET scores, a method that has been used previously to impute duration of walking and domestic activity sessions. [START_REF] Stamatakis | Temporal trends in physical activity in England. The Health Survey for England 1991 to 2004[END_REF] Convergent validity analyses are available in an appendix online. The validity of the physical activity questionnaire is also supported by objective measures of activity using accelerometry devices in 106 British adults (45 men) from the general population. [START_REF]Health Survey for England Physical Activity Validation Study: substantive report[END_REF]

Statistical analyses

White participants were substantially older than South Asians; to limit the confounding effects of age, this paper included only those aged between 18 and 55. To compare baseline characteristics between the two groups, independent sample t-tests were performed for continuous and χ 2 tests for categorical variables. Comparisons of totalMETs between ethnic groups used analyses of covariance, with ethnicity as a between-subject factor. Adjustments were made for age (modelled as a continuous variable), sex, body mass index (BMI), and self-reported health (excellent/good/fair/poor). To establish whether group differences were the result of ethnic or socioeconomic variations, education was also included in the model as a covariate. Sex by ethnicity interactions were added, the significance of which advocated examining sex-specific models. In addition, sportMETs were used as outcome measures to determine whether ethnic group variations were driven by differences in sports participation. Logistic regression analyses (adjusting for the same covariates as above) examined the government's recommendations for physical activity. Similar analyses were performed by age-group (18-35, 35-45, 45-55) to identify age rangespecific variations in physical activity. South Asian sub-groups were split by country of birth (India, Pakistan, and Bangladesh) and this division was used to explore sub-group differences in physical activity. Post hoc analyses using Fisher's Least Significant

Difference tests were performed. White British were included in these tests to establish whether physical activity differences observed previously were true ethnic group differences or whether sub-group variation was important. The South Asians were divided into 'UK born' and 'born elsewhere', and ANCOVAS (adjusting for covariates as before) were conducted to highlight the impact of westernisation on activity levels. All analyses were performed using SPSS 14.0. .001), lower levels of education (p < .001), and a higher proportion worked in manual occupations (p < .001).

RESULTS

Demographic information

Health data

BMI was significantly higher in whites compared with South Asians (p < .001).

Conversely, waist-hip ratio was highest in the South Asians (p < .001). South Asians were more likely to report their health as poor/very poor (p < .001), although there was no ethnic group difference in the proportion of people suffering from limiting longstanding illnesses. Psychological distress was higher in South Asians than their white counterparts (p <.001). A quarter of South Asian participants had previously smoked compared with nearly two-thirds of whites (p < .001).

Physical activity (table 2)

Physical

Sub-groups (table 3)

Sub-group analyses were performed separately by sex. In men, there were no sub-group differences in total physical activity between Pakistani, Bangladeshi and Indian men. The trend indicated Pakistani men reporting the lowest levels of exercise, then Bangladeshis, followed by Indians. This remained significantly lower than white men. Among women, as well as the ethnic group difference, Bangladeshi women reported lower levels of physical activity than women born in India (p = .040) and

Pakistan (p = .028). In terms of sportMETs, Bangladeshis performed significantly less exercise compared with whites (p < .001) and Indians (men: p = .046; women: p = .005).

Country of birth

The comparison of South Asians born in the UK with those born outside the UK (table 3) demonstrated a marked difference in physical activity (F (1, 4507) = 49.73, p < .001), despite adjustment for age, sex, BMI, and self-reported health. A sex by country of birth interaction term was significant (F (1, 4507) = 24.41, p < .001); therefore sexstratified analyses were conducted. There were group differences across men and women, with people born in the UK reporting higher levels of activity than those born outside the UK (F (1, 2134) = 59.55, p < .001, and F (1, 2368) = 4.68, p = .031, respectively). However, the impact of country of birth on physical activity levels was much stronger in men than in women (partial η 2 = .012 compared with .002). The same pattern was true for total sports activities between UK born and non-UK born South .00

Data presented as adjusted means (standard error). Different superscript letters represent significant differences between groups. Partial η 2 values indicate effect size. Adjustments made for age, BMI and self-reported health.

Explanatory demographic variables

Marital status was elevated and access to a car was reduced in South Asians;

both variables were significantly related to physical activity (p = .003 and p < .001, respectively) but neither factor had an impact on the ethnic group difference. Living in urban environments was more common among South Asians and urbanisation was significantly associated with lower levels of overall physical activity compared with rural areas (p < .001). Although ethnic group remained significant, the inclusion of urbanisation reduced the variance explained by ethnicity from partial η 2 = .023 to .017.

South Asians were more likely to perceive their area as having good local leisure facilities, although this factor was not strongly related to physical activity (p = .071). Its inclusion did, however, influence the impact of ethnic group on physical activity (partial η 2 = .023 to .013). Psychological distress, which was elevated in South Asians, was negatively related to physical activity levels (p = .019); more distressed respondents were less likely to be active. Ethnicity remained a significant factor in this model, with a marginal effect on effect size (partial η 2 = .023 to η 2 = .020). Finally, those variables which moderated the effect of ethnicity on physical activity (urbanisation and psychological distress) were included as covariates and despite the effect size of ethnicity being attenuated (partial η 2 = .023 to .014), physical activity levels were still significantly lower in South Asians compared with whites (p < .001).

DISCUSSION

This study aimed to provide a comprehensive assessment of non-occupational physical activity profiles of South Asians living in England using nationally-representative data. Previous studies have not used representative populations, [START_REF] Knight | Insulin resistance, diabetes, and risk markers for ischaemic heart disease in Asian men and non-Asian in Bradford[END_REF] and have not explored sub-sets of the population in such depth. [START_REF] Mckeigue | Relationship of glucose intolerance and hyperinsulinaemia to body fat pattern in South Asians and Europeans[END_REF][START_REF] Hayes | Patterns of physical activity and relationship with risk markers for cardiovascular disease and diabetes in Indian, Pakistani, Bangladeshi and European adults in a UK population[END_REF] We have shown that, regardless of sex, age-and sub-group, and type of physical activity (general versus sport), South Asian people living in England perform significantly lower levels of physical activity compared with whites. The consistency of this ethnic group difference across every subset in this paper points towards a contributory factor for the elevated risk of CHD observed in this population.

Our findings support previous work highlighting low levels of exercise among UK South Asians. [START_REF] Fischbacher | How physically active are South Asians in the United Kingdom? A literature review[END_REF] The homogeneity of these findings across studies adds weight to these conclusions. Even after adjustment for socioeconomic variations, South Asians still performed less physical activity. This is an interesting result, since physical activity has a socioeconomic gradient [START_REF] Wardle | Socioeconomic differences in attitudes and beliefs about healthy lifestyles[END_REF] and ethnic minorities in the UK consistently occupy the lower social grades [START_REF] Williams | Psychosocial risk factors for coronary heart disease in UK South Asian men and women[END_REF] (subject to sub-group variations [START_REF] Nazroo | South Asian people and heart disease: an assessment of the importance of socioeconomic position[END_REF] ).

Other studies have demonstrated the exceptionally low levels of exercise among South Asian women; [START_REF] Fischbacher | How physically active are South Asians in the United Kingdom? A literature review[END_REF] we have reiterated this finding, irrespective of sub-group. In addition, the age-group analyses performed for this paper provide a clear indication that these ethnic group differences are not specific to certain ages, and persist across generations. To the best of our knowledge, this paper is the first paper to show such continuity across age-groups.

Some studies have shown heterogeneity in profiles of exercise across South Asian sub-groups; [START_REF] Rudat | Black and ethnic minority groups in England: health and lifestyles[END_REF][START_REF]Black and ethnic minority groups in England: the second health and lifestyles survey[END_REF] [START_REF] Craig | Eating habits[END_REF] and report more psychological distress and lower social support [START_REF] Natarajan | Self-reported health and psychosocial wellbeing[END_REF] than Indians. Cumulatively, this evidence highlights the non-uniform CHD risk across subgroups, corroborating the observation that Pakistanis and Bangladeshis suffer elevated CHD and diabetes rates. [START_REF] Williams | Psychosocial risk factors for coronary heart disease in UK South Asian men and women[END_REF][START_REF] Bhopal | Heterogeneity of coronary heart disease risk factors in Indian, Pakistani, Bangladeshi, and European origin populations: cross sectional study[END_REF] The final analyses attempted to provide some indication of factors which might explain this ethnic group difference in physical activity. Factors such as urbanisation and psychological distress may partially explain the ethnic group difference in physical activity rates, however, ethnicity remained a significant risk factor. Thus, if area-and individual-level demographic and socioeconomic factors do not fully explain the low rates of physical activity in South Asian samples compared with whites, it would suggest that cultural explanations may be appropriate. This is supported by the finding that those South Asians born in the UK performed considerably more exercise compared with those born elsewhere. Despite this very crude measure of acculturation, it is likely to demonstrate some assessment of westernisation, indicating that less traditional South Asians may adopt more active profiles. This implies that future generations of South Asians born and bred in England may increase their levels of exercise. Nevertheless recent data in 9-10 year olds suggests that British South Asian children have lower objectively measured physical activity levels than European whites and African-Caribbeans. [START_REF] Owen | Ethnic and gender differences in physical activity levels among 9-10-year-old children of white European, South Asian and African-Caribbean origin: the Child Heart Health Study in England (CHASE Study)[END_REF] The HSE offers an opportunity to explore rates of physical activity in a large sample which provides representative data of people living in England. This is a key strength of this paper, presenting findings that can be generalised across England.

Despite this, some limitations should be considered. Education was used as the SES marker in this paper, because more complete data was available for education than for income or Registrar General social class. We are aware of the issues associated with using education as a indicator of SES in South Asians, [START_REF] Davey-Smith | Ethnicity, health and the meaning of socio-economic position[END_REF] however tests were performed to check the correlation between education and other markers of SES. Education had similar strength of associations with income and occupation in both ethnic groups.

Additional analyses using income and occupational grade as the SES markers in the models revealed similar results, i.e. the ethnic group differences in physical activity remained regardless of socioeconomic marker (data not shown). There are numerous concerns about the accurate measurement of physical activity, 35 however the interviewbased questionnaire used in the HSE comprised a comprehensive assessment of intensity, duration, frequency and type of activity and was validated using objective assessments. The self-reported physical activity questions have not been specifically validated in a South Asian population, although we have observed a clear and strong inverse association of resting heart rate with self-reported physical activity levels (p < .05) in all ethnic groups that would support the convergent validity of the questionnaire.

There is also the possibility that there may be cultural or ethnic differences in the interpretation of physical activity intensity measures, unfortunately there is no previous work exploring this issue in a South Asian population.

This paper has shown that physical activity is consistently low across UK South Asian people, regardless of sex, age, and sub-group. Cultural factors are likely to be partly responsible. It is essential that the health benefits of exercise are disseminated to South Asian communities nationwide, and that health professionals promote increased physical activity as a priority in this population.

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd to permit this article (if accepted) to be published in JECH and any other BMJPGL products and sublicences such use and exploit all subsidiary rights, as set out in our licence (http://jech.bmj.com/ifora/licence.pdf).

Finally, exploratory analyses

  included socio-demographic factors (marital status, car ownership, psychological distress, urbanisation, and local leisure facilities) in the full sample ANCOVA models, to identify additional variables which might account for a proportion of the ethnic group differences. Data are presented as means with standard deviation or standard error, or as percentages. Partial η 2 values indicate effect sizes.

Table 3 :

 3 Asian men and women (F (1, 2134) = 55.52, p < .001, and F (1, 2368) = 35.04, p < .001, respectively). ANCOVA results showing impact of subgroup and country of birth on total MET minutes per week (totalMETs)

Table 1

 1

	with 34% born in India, 31% in Pakistan, and 31% in Bangladesh. Over two-thirds were
	Muslim (70%), 19% Hindu and 10% were Sikhs. South Asians were more likely to be
	married (p < .001) and have more children (p < .001), however they were less likely to

presents the descriptive characteristics of the sample. The average age of the sample was 36.49 (SD 10.41); whites were still significantly older than South Asians, despite the age-range restriction (p < .001). Both groups had a similar sex distribution. Of the South Asian sample, a quarter were born in the UK compared with 81% of whites. Migrant South Asians had lived in the UK for an average of 26 years, own their homes (p < .001) or have access to a car (p < .001). Half of the South Asian sample was working, compared with over three-quarters of the white group (p < .001).

In terms of socioeconomic markers, South Asians reported lower household income (p <

Table 1 : Descriptive characteristics of sample

 1

	South Asians	Whites
	(n = 5421)	(n = 8974)

*

Values in means and standard deviations, and %. † Only includes South Asians born outside the UK. p values represent ethnic differences. ** p < .001.

table 2 )

 2 The with increasing age (partial η 2 = .003 in the youngest group compared with partial η 2 = .028 in the oldest group).

	Total sportMETs demonstrated a considerable ethnic group difference (F (1,
	12704) = 283.87, p < .001); the average total MET-minutes per week expended during
	sporting activities per week were 194.06 (SD 6.17) in South Asians compared with
	325.65 (SD 4.52) in whites. For sportMETs, the interaction between ethnic group and
	sex was not significant, and was therefore removed from the analyses. Analyses
	applying the governmental recommendations for physical activity (>450 MET minutes
	per week, moderate-vigorous activity) showed that South Asian people were 60% less
	likely to comply with the current guidelines (OR = .41, 95% CI .38-.45).
	Age-groups (

activity was markedly lower (by approximately 60%) in South Asians compared with whites, and this difference remained significant after controlling for age, sex, education, adiposity, and self-reported health variations (F (1, 12704) = 94.70, p < .001). An interaction effect for ethnicity and sex (F (1, 12704) = 19.01, p < .001) was observed. Thus, these analyses were performed separately by sex. Similar patterns were observed for both sexes, with South Asians displaying lower activity compared with whites (for men: F (1, 5884) = 179.27, p < .001; for women: F (1, 6816) = 121.23, p < .001). same pattern of low levels of physical activity was replicated in each of agegroup analyses, 18-35, 35-45, and 45-55 (F (1, 5423) = 121.02, p < .001, F (1, 3904) = 135.50, p < .001, and F (1, 3364) = 39.53, p < .001, respectively). Self-reported health became more important

Table 2 : ANCOVA results showing impact of ethnic group on total MET minutes per week

 2

			South Asians	Whites	Partial η	2
	Full sample		973.21 (22.44)	1465.93 (16.43)**	.01
	Men		1051.69 (36.36)	1670.94 (27.15)**	.03
	Women		909.17 (27.37)	1289.51 (19.66)**	.02
	Age groups:	18 -35	1043.54 (29.58)	1484.83 (26.39)**	.02
		35 -45	906.36 (43.70)	1528.80 (29.21)**	.03
		45 -55	962.27 (51.76)	1344.82 (29.69)**	.01

Data presented as adjusted means (standard error). ** p < .001. Partial η 2 values indicate effect size. Adjustments made for age, sex and sex by ethnic interactions (full sample), BMI and selfreported health.

  a similar trend was observed in this work, with Indian people generally engaging in more exercise than Pakistanis and Bangladeshis. Our finding that Pakistanis and Bangladeshis have worse physical profiles is commensurate with other health behaviour profiles documented in the HSE 2004 report. Pakistanis and Bangladeshis report greater use of tobacco products, 8 consume fewer fruits and vegetables,

Shephard RJ, Vuillemin A. Limits to the measurement of habitual physical activity by questionnaires * Commentary. Br J Sports Med 2003; 37:197-206.

ACKNOWLEDGMENTS

This work was funded by the National Prevention Research Initiative (Grant no. G0701859). The funding partners relevant to this award are: the British Heart Foundation; Cancer Research UK; Department of Health; Diabetes UK; Economic and Social Research Council; Medical Research Council; Research and Development Office for the Northern Ireland Health and Social Services; Chief Scientist Office; Scottish Executive Health Department; The Stroke Association; Welsh Assembly Government and World Cancer Research Fund. The Health Survey for England was commissioned by the Department of Health and was carried out by the Joint Health Survey Unit of National Centre for Social Research (NatCen) and Department of Epidemiology and Public Health at University College London.

COMPETING INTERESTS

All authors declare that the answer to the questions on the competing interest form are all 'No' and therefore have nothing to declare.

What is already known about this topic

UK South Asian groups suffer elevated risk of coronary heart disease compared with other ethnic groups in the UK. Physical inactivity is a major risk factor for coronary heart disease.

What this study adds

Using a nationally-representative sample, this study shows that, compared with the UK White population, physical activity is low in all groups in the South Asian population, regardless of sex, age group, sub group, and type of activity. This physical inactivity is likely to contribute to the excess coronary heart disease mortality observed in UK South Asian people.