Silvia Stringhini
email: stringhini<silvia.stringhini@inserm.fr

Aline Dugravot

Mika Kivimaki

Martin J Shipley

Marie Zins

Marcel Goldberg

Jane E Ferrie

Archana Singh-Manoux

Do different measures of early life socioeconomic circumstances predict adult mortality? Evidence from the British Whitehall II and French GAZEL studies

Keywords: Body height, early life, cohort studies, education, mortality, occupational position Adult, Aged, Cardiovascular Diseases, mortality, Cause of Death, Cohort Studies, Female, Follow-Up Studies, France, epidemiology, Great Britain, epidemiology, Humans, Male, Middle Aged, Neoplasms, mortality, Occupations, Proportional Hazards Models, Risk, Risk Factors, Social Class

Background

Father s occupational position, education and height have all been used to examine the effects of adverse early life socioeconomic ' circumstances on health, but it remains unknown whether they predict mortality equally well.

Methods

We used pooled data on 18393 men and 7060 women from the Whitehall-II and GAZEL cohorts to examine associations between early life socioeconomic circumstances and all-cause and cause-specific mortality.

Results

During

the 20-year follow-up period, 1487 participants died. Education had a monotonic association with all mortality outcomes, the age, sex and cohort adjusted Hazard Ratio (HR) for the lowest versus the highest educational group was 1.45 (95 Confidence % Interval (CI): 1.24,1.69) for all-cause mortality. There was evidence of a U-shaped association between height and all-cause, cancer and cardiovascular mortality, robust to adjustment for the other indicators (HR 1.41; 95 CI: 1.03,1.93 for those = % shorter-than-average and HR 1.36; 95 CI: 0.98,1.88 for those taller-than-average for cardiovascular (CVD) mortality). Greater = %

all-cause and cancer mortality was observed in participants whose father s occupational position was manual rather than non-manual ' (HR 1.11; 95 CI: 1.00,1.23 for all-cause mortality), but the risks were attenuated after adjusting for education and height.

= %

Conclusions

The association between early life socioeconomic circumstances and mortality depends on the socioeconomic indicator used and the cause of death examined. Height is not a straightforward measure of early life socioeconomic circumstances as taller people do not have a health advantage for all mortality outcomes.

Social inequalities in health have been reported and documented for several decades , , both in high-and low-income countries .

[

A key finding is the existence of a social gradient, in that there is a finely graded association between indicators of socio-economic position (SEP) and health . Another finding is that socioeconomic circumstances across the lifecourse are associated with adult [4 -7 ] health outcomes . At least three conceptual models have been identified within the lifecourse framework to explain the association [START_REF] Kuh | Introduction: A lifecourse approach to the aetiology of chronic diseases . A lifecourse approach to chronic disease epidemiology[END_REF] between health and socioeconomic circumstances over the lifecourse . These are the critical or sensitive periods model, the [START_REF] Ben-Shlomo | A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives[END_REF] accumulation model, and the pathways model. The critical or sensitive periods model views specific biological (e.g. low birthweight) or developmental factors at critical or sensitive periods of development to have a lifelong influence on health; the term sensitive period is used when the effects of adverse exposures are more amenable to modification. The accumulation model proposes that disadvantage at different points in the lifecourse has a cumulative dose response association with health. The pathway model views early environment to be important, but only because it shapes and influences the socioeconomic trajectories of individuals. The three lifecourse models are not competing hypotheses, as it has been shown that they are interrelated in such a way that it is impossible to disentangle them empirically [ , . In this paper, we focus on the effects of early life socioeconomic circumstances on health, without trying to separate the effect of 10 11 ] early life SEP from the effect of adult or lifecourse SEP.

Adverse childhood socioeconomic circumstances have been found, even in recent studies involving young cohorts, to be related to higher mortality for stomach, liver and lung cancer and for coronary heart disease (CHD), stroke and chronic obstructive pulmonary disease . Some studies have found childhood socioeconomic circumstances to be weak predictors of adult mortality, [12 -14 ] [12 ] especially cancer mortality , particularly in analysis adjusted for adult SEP .

[15 ] [START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF] Much research on the impact of early life socioeconomic circumstances uses retrospective measures or adult recall of socioeconomic circumstances earlier in the lifecourse in order to examine their impact on adult health. The particular measure of early life SEP used depends most often on what is available in a study. Measures of parental education and occupation have been used most often , , [12 16 -21 ] but studies have also used diverse markers such as housing conditions , childhood diet , , education , or [START_REF] Naess | Housing conditions in childhood and cause-specific adult mortality: the effect of sanitary conditions and economic deprivation on 55,761 men in Oslo[END_REF] [ [START_REF] Frankel | Childhood socioeconomic position and adult cardiovascular mortality: the Boyd Orr Cohort[END_REF] 23 ] [15 24 -30 ] anthropometric measures like adult height or leg length , , , . In this paper, we view early measures of socioeconomic [15 16 27 31 -35 ] position to reflect the period of life before the beginning of adult professional life. We use three measures of early life circumstances, father s occupational position, education, which being set early in life is considered to be an indicator of socioeconomic circumstances in ' early life , and height, which reflects cumulative effects of health conditions and disease load experienced during childhood and thus [START_REF] Galobardes | Measuring socioeconomic position in health research[END_REF] provides a proxy measure of childhood socioeconomic and environmental conditions .

[16 ]

This study has two objectives. First, to analyze three indicators of early life socioeconomic circumstances; father s occupational ' position, education and height, as predictors of mortality using pooled data from two occupational cohorts, the British Whitehall II study and the French GAZEL study. Second, to examine the associations of these measures of early life socioeconomic circumstances with specific causes of mortality.

MATERIAL AND METHODS

Study Population

The Whitehall II study was established in 1985 as a longitudinal study to examine the socioeconomic gradient in health and disease among 10,308 civil servants (6,895 men and 3,413 women) . All civil servants aged 35 55 years in 20 London based departments were de France (EDF-GDF). Further details of this study can be found elsewhere . At baseline, 20,625 (15,011 men and 5,614 women), [START_REF] Goldberg | Cohort profile: the GAZEL Cohort Study[END_REF] aged [START_REF] Batty | Adult height and lung function as markers of life course exposures: Associations with risk factors and cause-specific mortality[END_REF] classification and then categorized as non-manual (I II III NM) and manual (III M-IV V). In the GAZEL study it was assessed with the and Economic Studies (INSEE, ) and then http://www.insee.fr/fr/methodes/default.asp?page=nomenclatures/pcs2003/liste_n1.htm categorized as non-manual or manual.

was assessed as the highest qualification attained while in full-time education and grouped in 3 categories: university Education degree, higher secondary school, lower than higher secondary school. The Whitehall II measure of education was drawn from Phase 5 (1997 1999) as this measure was more detailed than the baseline measure. For non-responders at Phase 5 we used the baseline measure of education. In GAZEL, education was assessed at baseline. was measured, in Whitehall II, to the nearest millimeter at the baseline medical screening using a stadiometer with the Height participant standing completely erect with the head in the Frankfort plane. In the GAZEL study, height was self reported to the nearest centimeter in the baseline questionnaire. For the analysis, height was divided into 3 categories, separately in men and women and in the two cohorts, with the mid category composed of those of mean height 2cm and the other two categories representing those taller and ± shorter than those in the mean 2cm category.

±

Mortality

Whitehall II 10,297 (99.9 ) respondents were traced for mortality through the national mortality register kept by the National Health Services % Central Registry, by using the National Health Service identification number assigned to each British citizen. Mortality follow-up, including the causes of death, was available until 31 January, 2008; a mean of 20.3 years.

st

GAZEL

All participants were traced for mortality and information on vital status was obtained from EDF-GDF itself as it pays out retirement benefits. Data were available until 28 February 2008, a mean of 18.7 years, for all-cause mortality. In France, information on the th underlying causes of death, recorded by France s national death registry (INSERM-C piDC), is available two years later than the data on ' é all-cause mortality. Therefore, data on specific causes of death were available until 29 December, 2005.

th For both cohorts, we analyzed all-cause mortality, cancer mortality, Cardiovascular Disease (CVD) mortality, CHD mortality and other mortality (non cancer and non CVD). The International Classification of Disease (ICD) codes used to define Cancer were 140.0-209.9 (ICD 9) and C00-C97 (ICD 10), to define CVD were 390.0 458.9 (ICD 9) and I00-I99 (ICD 10), and to define CHD were 410.0 --414.9 (ICD 9) and I20-I25 (ICD 10).

Statistical Analysis

We pooled data from the two cohorts to obtain sufficient statistical power for the analysis. As a preliminary step we undertook initial analyses on the two cohorts separately and then, using the pooled data, we tested the interaction terms between measures of early life socioeconomic circumstances and cohort for each mortality outcome. These analyses showed no evidence of any difference in the association between socioeconomic circumstances and mortality between the two cohorts, allowing us to estimate the effects of early life socioeconomic circumstances using pooled data from the two cohorts. We tested the interaction terms between the measures of early life socioeconomic circumstances and sex for each mortality outcome. No significant interactions were found, so data for men and women were analyzed together. We examined the extent to which the three measures of socioeconomic circumstances were associated with each other using Pearson s chi-squared test. We examined the age standardized mortality rates per 1,000 person-years for each indicator -father

' '
s occupational position, education and height categories -for all cause and cause-specific mortality.

Cox regression was used to estimate Hazard Ratios (HR) and their 95 percent confidence intervals (CI) for the associations between measures of early life socioeconomic circumstances and mortality. Participants were censored at the date of death or at the end of follow-up, whichever occurred first. For cause-specific analysis, GAZEL participants were censored at the date of death or at 29 th December 2005. Each measure, father s occupational position, education and height, was first analyzed separately with adjustment for age, ' sex and cohort. Subsequently, in order to assess whether the SEP markers were associated with mortality independently of each other all measures of early life socioeconomic circumstances were entered simultaneously into the Cox model in addition to age, sex and cohort.

Tests for linear trend across categories of early life socioeconomic circumstances were performed by entering them as continuous variables. The proportional hazard assumptions associated with Cox regression were tested by use of Schoenfeld residuals and found not to be violated at 0.05. P = All analyses were performed using statistical software STATA 10, StataCorp LP, Texas, USA.

RESULTS

Participants with missing data on father s occupational position (1,914), education (1,626) and height (3,109), or not followed-up for ' mortality [START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF], categories not mutually exclusive, were excluded from the analysis. In total, 18,393 men (84.0 of men at baseline) and % 7,060 women (78.2 of women at baseline) were included in the analysis presented here. Those excluded tended to be shorter (169.5 cm % vs. 171.2 cm, <0.001), more likely to have a father with a manual occupation (57 vs. 54 , 0.006), and less educated (31 vs. 29 P

% % P = % %
with no higher secondary education, <0.001). The excluded men were not different in age to those included (44.0 vs. 44.0 years, P P = 0.89), but excluded women were older than included women (43.0 vs. 42.7 years, <0.01). For three individuals the specific cause of P death was unknown and they have been excluded from the cause-specific analysis.

shows the sample characteristics. As expected, the cohorts have marked differences in father s occupational position and Table 1 ' education with Whitehall II being the more advantaged cohort. For father s occupational position, the majority of the Whitehall II ' participants (60.9 of men and 50. [START_REF] Mackenbach | Socioeconomic inequalities in morbidity and mortality in western Europe[END_REF] of women) reported non-manual occupations whereas the majority of the GAZEL participants

% % 4 12
(61.9 of men and 55.7 of women) reported their father to have a manual occupation. All three markers of early life socioeconomic % % circumstances were closely related. In both studies, participants whose father had a non-manual occupation or participants with a higher education tended to be taller ( <0.001, results not shown). Similarly, participants with a higher education were more likely to report father P s occupation as non-manual ( <0.001, results not shown).

' P A total of 389 men and 185 women in Whitehall II and 772 men and 141 women in GAZEL died during the follow-up. The mortality rate for men was the same in both cohorts (3.3 per 1,000 person-years) but higher in Whitehall II than in GAZEL for women (3.5 vs. 1.7 per 1,000 person-years). Information on the underlying cause of death was available for 571 of the 574 deaths in Whitehall II participants and 716 out of 913 in GAZEL.

shows the mortality distribution across the measures of early life socioeconomic circumstances for all-cause mortality. As Table 2 mortality was lower in the average-height category (mean 2cm) it was taken to be the reference category for height; for other indicators, ± non-manual father s occupational position and university degree were the reference categories. In the analysis adjusted for age, sex and ' cohort, participants whose father had a manual occupation had a slightly higher risk of death (HR 1.11; 95 CI: 1.00, 1.23) compared to = % those whose father s had a non-manual occupation. The risk was attenuated after adjusting for education and height. The association ' between education and mortality was robust and only slightly attenuated after adjustment for height and father s occupation. The test for ' trend suggested a linear association between education and mortality ( 0.001). The Cox regression suggested some evidence of higher P ≤ mortality in the shorter-than-average (HR 1.17; 95 CI: 1.02, 1.34) and taller-than-average individuals (HR 1.16; 95 CI: 1.01, 1.34).

= % = %
These associations remained unchanged after adjustment for the other measures of early life socioeconomic circumstances.

shows analyses using cancer mortality as the outcome. The associations were largely similar to those for all-cause mortality. Table 3 Participants whose father had a manual occupation had a 19 (95 CI: 1.01, 1.39) higher risk of dying from cancer. Compared to

% %
participants with a university qualification, those with less than higher secondary education had 60 (95 CI: 1.26, 2.04) higher risk of

% %
death. The U-shaped association between height and mortality was also evident (HR 1.24; 95 CI: 1.01, 1.53 for the = %

shorter-than-average and HR 1.30; 95 CI: 1.05, 1.61 for the taller-than-average). The associations between height and cancer mortality

= %
were not reduced after adjusting for the other measures of early life socioeconomic circumstances.

presents the association between early life socioeconomic circumstances and CVD mortality, with CHD as a separate Table 4 sub-category. The lowest education group had greater risk of CVD mortality in the fully adjusted model (HR 1.41; 95 CI: 1.01, 1.97)

= %
and there was some evidence of a dose-response effect ( 0.06). Shorter-than-average individuals had a greater risk of CVD mortality P = (HR 1.41; 95 CI: 1.03, 1.93) and there was also an indication of excess risk of CHD mortality (HR 1.40; 95 CI: 0.94, 2.09).

= % = %
Similarly, there was some evidence of excess CVD (HR 1.36; 95 CI: 0.98, 1.88) but not CHD (HR 0.97; 95 CI: 0.62, 1.51)

= % = %
mortality among the taller-than-average individuals. Finally, presents the results for other causes of death, or non cancer and non Table 5 CVD mortality. Although none of the results were significant at the <0.05 level, the pattern of results was similar to that for all-cause P mortality, except for height which tended to be inversely associated with mortality.

DISCUSSION

This study, using pooled data from two populations followed-up for approximately 20 years, shows that three markers of early life socioeconomic circumstances, father s occupational position, education and height, are differently related to mortality and may therefore ' capture different aspects of early life conditions. The three measures were correlated with each other but their predictive ability was different, with education being the only indicator consistently associated with mortality. Participants whose father had a manual occupation had a slightly higher risk of all-cause and cancer mortality. There was evidence of a U-shaped association between height and all-cause mortality, cancer mortality and CVD mortality.

Father s occupational position '

Participants whose father had a manual compared to a non-manual occupation were in general at higher risk of mortality. This excess risk was greatly attenuated when adjusted for the effects of education and height. Further analyses (not shown) revealed the attenuation to come primarily from adjustment for education, as has been reported previously . Sensitivity analyses with finer categorization of father [START_REF] Lawlor | Association of childhood socioeconomic position with cause-specific mortality in a prospective record linkage study of 1,839,384 individuals[END_REF] ' s occupation did not show any evidence of a dose-response association with mortality.

Education

Education had the most consistent association with mortality, with evidence of excess mortality in the lower education groups for all mortality outcomes examined. In general terms, there was a dose-response association between education and mortality. This association was not reduced after adjustment for father s occupational position and height and this can be explained in different ways. First, education ' strongly relates to adult socioeconomic position and thus material conditions in adulthood . Second, education is thought to have a [START_REF] Galobardes | Indicators of socioeconomic position (part 1)[END_REF] direct effect on health through health behaviors . Finally, education is easier to measure accurately and to assign to individuals than [START_REF] Lynch | Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse[END_REF] several other SEP indicators , and misclassification is less likely to occur. The role of education in predicting mortality was especially [START_REF] Galobardes | Indicators of socioeconomic position (part 1)[END_REF] marked for cancer mortality.

Height

Our data suggest a U-shaped association between height and mortality. Adult height is often used as a proxy measure of early life socioeconomic circumstances with the assumption that short stature results from poor circumstances or during early life , . in utero [12 41 ] This hypothesis has been supported by results showing an inverse association between adult height and all-cause mortality, cardiovascular mortality and other causes of death related to early life conditions , , , . This has led to the assumption of a linear association [31 32 42 43 ] between height and health. Our data show this to be the case for CHD mortality, as in other studies . In a sub-sample of the Whitehall [START_REF] Jousilahti | Relation of Adult Height to Cause-specific and Total Mortality: A Prospective Follow-up Study of 31, 199 Middle-aged Men and Women in Finland[END_REF] II study (N 4,546) we had data on components of height. Analyses using these data showed leg length to have a linear association with = CHD mortality, one standard deviation decrease in leg length was associated with 25 excess risk of CHD mortality (95 CI 23 to

% % = -% 103
).

%

Our results on CVD mortality do not suggest a linear association with height. Although short people were at higher risk for CVD mortality, as has been shown previously , , tall people were also found to be at higher risk compared to people of average stature. [32 44 ] The U-shaped association could be explained by the inclusion of a range of causes of death considered under CVD mortality. For instance, previous research suggests greater risk of aortic aneurysm among the tall individuals . On the other hand, studies on stroke incidence Our study supports existing literature on the higher risk of cancer mortality among the taller individuals. This finding has been shown consistently for breast cancer, colorectal cancer, prostate cancer and cancers not related to smoking . Several plausible mechanisms [START_REF] Gunnell | Height, leg length, and cancer risk: a systematic review[END_REF] have been suggested for the association between height and cancer. For example, taller people have larger organs and therefore their chances of dividing stem cells undergoing transformation to malignancy might be raised; some genetic factors linked with height may also be tied to tumor risk; and height might reflect early life dietary or metabolic factors that affect cancer risk .

[47 ]

We also found a higher risk for cancer mortality among those shorter than the average. There is some evidence of greater risk among the shorter individuals for certain cancers, as gastrointestinal cancers or cervical cancer , but results remain inconsistent. In the

[48 ] [32 ]
Whitehall II sub-sample for which components of height were examined, trunk length seemed to be better related to cancer mortality than leg length, for instance a standard deviation increase in trunk length was associated with 20 greater mortality (95 CI: 3 to 47 ). The % % -% % U-shaped relationship between height and mortality could also be explained by the fact that some cancers, such as smoking related or esophagus and stomach cancers, have been related to low SEP and others, such as breast and colon cancer and skin melanoma to high SEP .

[49 ]

As suggested in previous research , , , , , , , the association between short stature and mortality seems to be [12 31 32 41 -43 47 50 51 ] driven by living conditions in early life while the association between greater stature and mortality is probably explained by other non-social mechanisms. Therefore, height can be a misleading indicator of early life socioeconomic circumstances when used in relation to mortality. Mechanisms behind the relation of height with mortality still need to be understood, and further research on this topic is needed.

Strengths and limitations

The major strength of this study is that it uses data from two different populations, allowing sufficient power to examine the differential predictive ability of the three markers of early life socioeconomic circumstances for premature adult mortality. However, there are several potential limitations to this study that should be considered in interpreting results. Pooling data from two cohorts increased power in the analysis, but the measures used in this study are not identical in the two populations. In an effort to harmonize the measures we have used broad categories for the indicators of early life socioeconomic circumstances. Given the size of the original cohorts, the GAZEL participants represented a much higher proportion (66 ) of the study population. Results may therefore have been influenced % more by the French GAZEL population even though the interaction term did not suggest any difference between the cohorts. Pooling all cancers is not an ideal strategy as social inequalities differ by cancer site. However, in the present study we did not have sufficient power to examine different cancers separately.

A major drawback of the majority of the studies in this domain, including ours, is that information on early life circumstances is collected through adult s recall. Moreover, in our study the question on father s occupation did not relate specifically to the participant s approximately 0.40 cm. We created the height categories using measures to the nearest centimeter and it is unlikely that an overestimation of this magnitude could cause a significant bias in the estimations.

Analyses reported here were based on only 82 of participants. As participants not included had a lower socioeconomic profile, their % exclusion may have led to some underestimation of the observed effects. Finally, it has to be considered that both occupational cohorts examined here differ from the majority of other studies as they are based on individuals with stable jobs. This implies that the part of the population who suffered adverse socioeconomic circumstances in early life and eventually ended up with temporary jobs or unemployed is not represented in this study.

Conclusions

Father s occupational position, education and height have all been used as indicators of early life socioeconomic circumstances. Our ' results suggest that their association with mortality 1) does not have the same shape, 2) depends on the cause of mortality being examined, and 3) underlying mechanisms linking them to health may not be the same.

WHAT THIS PAPER ADDS

*What is already known on this subject?*

Adverse childhood socioeconomic circumstances have been found, even in recent studies involving young cohorts, to be related to higher mortality from all-causes, stomach, liver and lung cancers and for coronary heart disease (CHD), stroke and chronic obstructive pulmonary disease.

*What does this study add?*

Our results show that the associations between the indicators of early life socioeconomic circumstances and mortality 1) do not have the same shape and 2) depend on the cause of mortality being examined.

Ackowledgements:

The authors express their thanks to EDF-GDF and to the Caisse centrale d action sociale du personnel des industries electrique et gaziere .

" ' "

We also wish to acknowledge the team of the Unit 687 Inserm and of the Whitehall II study responsible for the cohorts data base cohortes é '

management.

Footnotes:

  by letter, 73 agreed. Baseline examination (Phase 1) took place during 1985 1988, and involved a clinical % examination and a self-administered questionnaire containing sections on demographic characteristics, health, lifestyle factors, work characteristics, social support and life events. Participants were followed-up for mortality until 2008. The University College London Ethics Committee approved the study. The GAZEL study was established in 1989, on employees of France s national gas and electricity company: Electricit de France-Gaz ' é

question

  Which is /was your father s occupation , coded basing on the classification provided by the French National Institute of Statistics " ' "

  Data from these two studies were pooled. Both cohorts were set up in the late eighties and the mortality follow-up covers broadly the same period. Male participants in both cohorts are of a similar age but the women in GAZEL are on average 3.8 years younger.

	Participants in both cohorts have stable jobs; the GAZEL study is composed of both blue and white collar workers and Whitehall II only of
	white collar workers.
	Measures
	Father s occupational position '	was assessed at the baseline survey in both studies. In Whitehall II it was assessed with the question	"
	What is/was your father s main job, what kind of work does/did he do in it , coded basing on the Registrar General s Occupational position ' " '

50, gave consent to participate in this study. The study design consists of an annual questionnaire used to collect data on health, lifestyle, individual, familial, social and occupational factors and life events. Participants were followed-up for mortality until 2008. The GAZEL study received approval from the national commission overseeing ethical data collection in France (Commission Nationale Informatique et Libert ).

é

  of this measure for mortality. Other measurement limitations include the use of a self reported measure of height in the GAZEL study. A study conducted in 1999 on the validity of self reported height in the GAZEL study found that height was overestimated by

	[56 ]
			'	'	'
	childhood. Two studies	, [52 53 ]	have recently shown that adult s recall of father s occupational position during early life might be ' '
	overestimated. Misclassification of father s occupation may lead to underestimation of its true effect on adult health '	[54 ]	. In fact, other
	studies	, [19 55 ]	where information on father s occupation was obtained from registers during early life showed a greater predictive ability '

Table 1

 1 MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health. The GAZEL Cohort study was funded by EDF-GDF and INSERM, and received grants from the "Cohortes 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 or government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in JECH editions and any other BMJPGL products to exploit all subsidiary rights, as set out in 55 . Davey Smith G , McCarron P , McEwen J . Social circumstances in childhood and cardiovascular disease mortality: prospective observational study of Glasgow University students . J Epidemiol Community Health . 2001 ; 55 : 340 -341 56 . Niedhammer I , Bugel I , Bonenfant S . Validity of self-reported weight and height in the French GAZEL cohort . Int J Obes Relat Metab Disord . 2000 ; 24 : 1111 -1118 Characteristics of Participants of the Whitehall II Study and the GAZEL Study

	Sant TGIR Program	and the	.
	our licence (	http://jech.bmj.com/ifora/licence.pdf	).
	References:

FUNDING The Whitehall II study has been supported by grants from the British Medical Research Council (MRC); the British Heart Foundation; the British Health and Safety Executive; the British Department of Health; the National Heart, Lung, and Blood Institute (grant HL36310); the National Institute on Aging (grants R01AG013196;R01AG034454 ); the Agency for Health Care Policy and Research (grant HS06516); and the John D. and Catherine T.

é "

Agence Nationale de la Recherche AS-M is supported by a European Young Investigator Award from the " "

European Science Foundation. JEF is supported by the MRC (Grant G8802774). MJS is supported by the British Heart Foundation. # COMPETING INTERESTS None LICENCE STATEMENT = WOMEN (n 4,457) = Father s position N ( ) = * Rate per 1,000 person-years

Table 2

 2 Early life Socioeconomic Predictors of All-cause Mortality, the Whitehall II and GAZEL Studies (N 25,453) Age adjusted rate per 1,000 person-years ** Adjusted for age, sex, cohort and mutually adjusted.

	=
	Adjusted for age, sex and cohort	Fully Adjusted **

%

HR (CI 95 )

%

N

Rate * *

Table 3

 3 Early life Socioeconomic Predictors of Cancer Mortality, the Whitehall II and GAZEL Studies(N 25,450)

=

Adjusted

for age, sex and cohort Fully Adjusted ** Early life socioeconomic measures Deaths HR (CI 95 )

  Age adjusted rate per 1,000 person-years ** Adjusted for age, sex, cohort and mutually adjusted.

	'
	Non Manual	276	(1.23)	1.00	-	1.00	-
	Manual	378	(1.37)	1.19	1.01, 1.39	0.92	0.76, 1.11
	Education
	University	98	(0.90)	1.00	-	1.00	-
	Higher Secondary	310	(1.34)	1.59	1.26, 2.01	1.56	1.24, 1.98
	Lower than Secondary	246	(1.68)	1.60	1.26, 2.04	1.55	1.22, 1.99
	Height (cm)
	Shorter than mean 2cm -	284	(1.40)	1.24	1.01, 1.53	1.23	1.00, 1.51
	Mean 2cm ±	131	(1.12)	1.00	-	1.00	-
	Taller than mean 2cm +	239	(1.42)	1.30	1.05, 1.61	1.31	1.06, 1.62

% HR (CI 95 ) % N Rate * Father s occupational position *

[START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF]

[START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF]

[START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF]

[START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF]

[START_REF] Galobardes | Childhood Socioeconomic Circumstances and Cause-specific Mortality in Adulthood: Systematic Review and Interpretation[END_REF]


Claire Macher
email: claire.macher@ifremer.fr

Jean Boncoeur
email: jean.boncoeur@univ-brest.fr

Optimal Selectivity and Effort Cost A Simple Bioeconomic Model with an Application to the Bay of Biscay Nephrops Fishery

Keywords: Selectivity, effort, technical measure, management tradeoff, Nephrops ishery, Bay of Biscay, bio-economic model, social and private beneits and costs, fuel costs JEL Classiication Codes Q22, Q57, H23

Optimal selectivity and effort cost: A simple bioeconomic model with an application to the Bay of Biscay Nephrops fishery

Claire Macher, Jean

Introduction

So-called "technical measures," aimed at increasing the selectivity of catches in the ishing industry, are an important component of the standard isheries management toolbox [START_REF] Suuronen | The Role of Technical Measures in European Fisheries Management and How to Make Them Work Better[END_REF]. Several social beneits are expected from this increased selectivity [START_REF] Pascoe | Costs and Beneits of Bycatch Reduction Devices in European Brown Shrimp Trawl Fisheries[END_REF]. The most widely acknowledged ones rely on the biological impact of selectivity measures. Selectivity has positive consequences on non-commercial species and on the biomass and age-structure of commercial stocks [START_REF] Beverton | on the Dynamics of Exploited Fish Populations[END_REF][START_REF] Kvamme | Assessing the Effects on Stocks of Selectivity Changes in a Fishery[END_REF][START_REF] Maclennan | Gear Selectivity and the Variation of Yield[END_REF][START_REF] Salini | Assessment and Beneits of Damage Reduction in Prawns Due to Use of Different Bycatch Reduction Devices in the Gulf of Carpentaria, Australia[END_REF][START_REF] Stergiou | Selectivity Experiments in the nE Mediterranean: The Effect of Trawl Codend Mesh Size on Species Diversity and Discards[END_REF][START_REF] Suuronen | The Role of Technical Measures in European Fisheries Management and How to Make Them Work Better[END_REF][START_REF] Vanmarlen | Technical Modiications to Reduce the By-catches and Impacts of Bottom-ishing Gears. The Effects of Fishing on Non-target Species and Habitats: Biological Conservation and Socio-economic Issues[END_REF][START_REF] Ward | The Bioeconomic Implications of a Bycatch Reduction Device as a Stock Conservation Management Measure[END_REF] 1 . Another important social beneit is due to the fact that rights-based systems of isheries management are easier to implement when ishing is selective; i.e., when unsought joint productions are minimized. Moreover, selectivity may generate some private beneits, such as minimizing time spent by crew sorting catches.

however, selectivity also generates costs that need to be considered when establishing the policy-mix characterizing the management of a ishery. Apart from the possible higher price of selective gears, a major source of costs arises from the use of more selective gears, which often reduce catches of marketable ish per unit of effort [START_REF] Fonseca | Using a Modiied nordmore Grid for By-catch Reduction in the Portuguese Crustacean-trawl Fishery[END_REF][START_REF] Kvamme | Assessing the Effects on Stocks of Selectivity Changes in a Fishery[END_REF][START_REF] Madsen | Selectivity Experiments with Escape Windows in the North Sea Nephrops (Nephrops norvegicus) Trawl Fishery[END_REF][START_REF] Tschernij | A Modelling Approach for Assessing Short-term Catch losses as a Consequence of a Mesh Size Increase[END_REF][START_REF] Vanmarlen | Technical Modiications to Reduce the By-catches and Impacts of Bottom-ishing Gears. The Effects of Fishing on Non-target Species and Habitats: Biological Conservation and Socio-economic Issues[END_REF]. This feature suggests that net beneits provided by selectivity depend on the level of ishing effort.

In this article, we address this issue with the help of a simple bioeconomic model, which we apply to the case of the French ishery of the Bay of Biscay. The model describes the harvesting of a commercial stock with a poorly selective gear (e.g., trawling). low selectivity usually results in jointly harvesting individuals belonging to several species, and, for each species, individuals belonging to several size or age classes. In the model, we simplify this feature by assuming that only different age groups of the same species are jointly harvested. Increasing selectivity (e.g., by using larger mesh size) reduces catch per unit of effort (CPUE) for younger age groups, which are also characterized by lower landings prices. The model allows computing the long-run beneits and costs of increasing selectivity for a given level of effort. It also makes it possible to determine the optimal policy-mix between effort and selectivity controls.

In the irst part of this article, we describe a simpliied version of the model with only two age groups. After presenting the assumptions of the model, we analyze the impact of increasing selectivity on equilibrium catches and landed values for a given level of effort. We then characterize the envelope curve representing landed value as a function of ishing effort, assuming optimal selectivity for each level of effort. Using this envelope enables the deinition of the optimal combination of selectivity and effort and to assess the impact of increased ishing costs on this combination.

In the second part of this article, we apply an extended version of the model to a simpliied representation of the French Nephrops ishery of the Bay of Biscay. We simulate the impact of an increase in fuel cost on the optimal level of ishing effort and optimal selectivity.

Concluding remarks are devoted to a comparison between simulation results, the current state of the ishery, and to the possibility of bridging the gap between private and social net beneits of selectivity.

Simpliied Theoretical Model

In this section, we present a simpliied version of the model, assuming the harvest is composed of only two age classes. This simpliication is aimed at presenting more clearly the basic mechanisms at work when increasing gear selectivity. The model is run under biological equilibrium conditions; i.e., assuming stabilized ish biomass for each age group in each case. As a result, expressions such as "increasing gear selectivity" are used here only for the sake of brevity; the analysis developed in this section is purely based on comparative static.

Assumptions

We assume that a single stock composed of two age groups (i = 1, 2), is ished by a homogenous leet making use of a given ishing technique. Time unit is the ishing season, which may be equal to a year, or shorter. Fishing effort (E) is held constant over time (i.e., during the whole ishing season and from season to season). For each age group, CPUE is assumed to be proportional to the corresponding ish biomass. As a result, the rate of ishing mortality in each age group (F i ) is proportional to ishing effort, and hence is also constant over time:

F i = q i E (i = 1,2), (1)
where q i ≥ 0 is the catchability coeficient concerning age group i. The value of this coeficient is conventionally set at 1 for the older group (q 2 ). For the younger group, it is deined as:

q 1 = 1 -s (0 ≤ s ≤1), ( 2
)
where s is a parameter characterising the selectivity of the ishing technique. If s is equal to one, ishing mortality for age group 1 is null, and the ishing technique is said to be fully selective, or, in short terms, selective. If s is lower than one, joint productions appear: ishing effort generates mortality for both age groups. Cases where 0 < s < 1 are described as situations of "imperfect selectivity." The level of selectivity decreases as s gets closer to zero. When this parameter reaches zero, the rate of ishing mortality for age group 1 reaches the same level as for age group 2, and the ishing technique is called "non-selective." For the sake of simplicity, we assume that changes in selectivity do not modify effort costs (e.g., the price and operating cost of a trawl are assumed to be independent of mesh size). Also for the sake of simplicity, changes in ish biomass owing to recruitment, individual growth, and natural mortality are assumed to occur once a year, at the beginning of the ishing season. Annual recruitment is exogenous. 2 We assume that it is constant and conventionally set the level of yearly recruited biomass at 1. When the stock is not ished, changes in biomass between age groups 1 and 2 are due to individual growth and natural mortality. We use a single positive parameter, a, to represent the combined impact of these two factors: a is equal to the biomass of age group 2 at the beginning of the ishing season, assuming age group 1 is not ished. It is larger than 1 if the impact of individual growth overrides the impact of natural mortality, and smaller than 1 in the reverse case.

All catches are supposed to be landed and marketed. Unit-cost of effort (c) and ex-vessel unit-prices (p i ) for landings of individuals of each age group (i = 1, 2) are exogenous, positive, and constant. We assume that:

p 2 a > p 1 .
(3) (Should this condition not be satisied, non-selective ishing would be optimal in any case.)

2 The exogenous character of recruitment is a questionable assumption in a model where spawning stock biomass (SSB) is endogenous. However, in many real-world isheries, no statistically signiicant relationship between SSB and recruitment may be drawn from available data, which may be explained by the reproductive strategy of a large class of marine commercial (and non-commercial) species. As a result of this strategy, recruitment depends much more on environmental (e.g., hydroclimatic) conditions that prevailed during the prerecruitment stage than on the number of genitors [START_REF] Chambers | Environmental Inluences on Egg and Propagule Sizes in Marine Fishes. Early Life History and Recruitment in Fish Populations[END_REF][START_REF] Hilborn | Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty[END_REF]ICES 2006ICES , 2008)). This feature, which fully applies to the stock used as a case study in the second part of this article, makes the simplifying assumption of exogenous recruitment more acceptable.

Impact of Selectivity on Catches

let (t = 0) be the beginning of the ishing season and X i (t) the biomass of age group i (i = 1, 2) at time t of the ishing season. Since the only factor of variation of this biomass during the ishing season is ishing mortality, the instantaneous rate of which is held constant, we can write:

X i (t) = X i (0)e -q i Et (i = 1,2). ( 4
)
The volume of catches over age group i during the whole season is given by:

Y i = X i (0) -X i (1) = X i (0)(1-e -q i E ) (i = 1,2).
(5)

let us now consider this function separately for each age group and assess the impact of a change in the selectivity parameter on the corresponding catches.

For age group 1, X i (0) is equal to recruited biomass, conventionally set at 1, and the catchability coeficient is given by relationship (2). Therefore, the above equation becomes:

Y 1 = 1 -e -(1-s)E . ( 6
)
When ishing is selective (s = 1), catches of individuals belonging to age group 1 are null (Y 1 = 0). With a selectivity coeficient lower than 1, catches of group 1 are an increasing and concave function of ishing effort, with an asymptotic level equal to recruited biomass as effort increases indeinitely. In this case, an increase in selectivity reduces catches of group 1 for any positive level of effort (igure 1). For age group 2, the catchability coeficient is conventionally set at 1, and the biomass at the beginning of the ishing season is equal to ae -(1-s)E . Therefore, equation ( 5) becomes:

Y 2 = ae -(1-s)E (1-e -E ). (7)
When ishing is selective, age group 1 is not harvested, and the biomass of age group 2 at the beginning of the ishing season is equal to a. In this case, catches of age group 2 are a monotonic increasing and concave function of ishing effort, with a as an asymptote when effort grows indeinitely (igure 2). With a selectivity coeficient lower than 1, the situation is more ambivalent, because an increase in ishing effort (under equilibrium conditions) has two opposite effects on catches of age group 2: a direct positive effect, symbolised by factor (1e -E ) in relationship ( 7), and an indirect negative effect, symb- olised by factor ae -(1-s)E in the same equation. The latter is due to the fact that increasing ishing effort with a non-selective or an imperfectly selective gear induces a decrease in the volume of age group 1 biomass at the end of the ishing season and, as a result, in the volume of age group 2 biomass at the beginning of the next season. The direct positive effect is dominant for a low level of ishing effort, but once effort has gone beyond the following threshold:

(8) the indirect negative effect becomes dominant, and catches of age group 2 become a decreasing function of ishing effort. These catches tend towards zero as effort increases indeinitely, because this implies that nearly all ish are caught at age 1 ("recruitment ishery").

The impact of an increase in selectivity on the relationship between effort and catches of age group 2 is depicted in igure 2. For any given level of effort, increased selectivity means increased catches of this group, because it lets a larger number of individuals reach age 2. Moreover, the level of effort corresponding to maximum catches of this age group increases (when s reaches 1; i.e., when ishing becomes fully selective, this level is ininitely high. As noted before, catches of group 2 are a monotonic increasing function of effort if ishing is selective).

Impact of Selectivity on Landed Value

The yearly landed value is deined as:

V = p 1 Y 1 + p 2 Y 2 . ( 9
)
Combining this relationship with ( 6) and ( 7), we get:

V = p 1 (1-e -(1-s)E ) + p 2 ae -(1-s)E (1-e -E ). ( 10
)
For given levels of selectivity and prices, this equation expresses landed value as a function of ishing effort (igure 3). In the case of full selectivity (s = 1), only individuals of age group 2 are landed, and V is a monotonic increasing and concave function of E, tending towards p 2 a as ishing effort increases indeinitely.

If the selectivity coeficient is lower than 1, ishing generates joint productions, and V becomes a weighted means of catches of age groups 1 and 2. While the former is a monotonic increasing function of ishing effort, we have seen that the latter is an increasing function of effort as long as , E E  and becomes a decreasing function of effort beyond this level, with a zero asymptote as effort grows indeinitely. As a result, provided condition (3) holds, the relationship between E and V is not monotonic. For low levels of effort, V is an increasing function of E. It is maximum when E reaches:

2 2 1 2 ˆln ln , 1 p a s E s p a p      (11)
If effort grows beyond that point, landed value starts decreasing. It tends monotonically towards p 1 when effort increases indeinitely, as catches concentrate on age group 1 (recruitment ishery). The maximum value of V:

    2 1 1 1 2 2 2 1 ( ) 1 , 2 s s s s p V E p p a p a s               (12)
is included between p 1 and p 2 a, which are the two asymptotic values of V, respectively, when effort grows indeinitely with imperfect selectivity (or non-selectivity) and full selectivity.

The way selectivity affects landed value varies according to the level of ishing effort. For low levels of effort, the negative impact of poor selectivity on the harvestable biomass of age group 2 is limited, and increasing selectivity merely results in decreased value of landings of age group 1. For higher levels of effort, the negative impact of poor selectivity on the harvestable biomass of age group 2 becomes more severe and may override its positive inluence on the landings of age group 1. Consequently, increasing selectivity may result in increasing total landed value. Differentiating (10) with respect to s shows that landed value is a decreasing function of selectivity for levels of effort that are lower than:

E ~ 2 2 1 p a ln p a p   ( 13
)
and becomes an increasing function of selectivity with higher levels of effort. note that E ~ , which is the level of effort resulting in V = p 1 , does not depend on s. In igure 3, the curve representing V(E) rotates counter clockwise around the point of coordinates (E ~ , p 1 ) as s is progressively increased from 0 to 1; i.e., as ishing technique evolves from non selectivity to full selectivity. During this evolution, the level of effort Ê maximising V increases, and so does the corresponding maximum value. When s tends towards 1, Ê tends towards ininite, and

V ( E ) tends towards p 2 a.
According to the level of effort in the ishery, selectivity and effort may be regarded as complementary or substitutive factors. For effort levels under E ~ , increasing selectivity reduces the value of landings ceteris paribus, but this effect may be offset by a simultaneous increase in effort; effort and selectivity may be considered as complementary. When effort is between E ~ and Ê , landed value is an increasing function of both selectivity and effort, which may then be regarded as substitutes (an increase in one of these factors may be used to balance the consequences of a decrease in the other). If effort goes beyond Ê , effort and selectivity become complementary once again. Increasing effort reduces landed value, ceteris paribus, but increasing selectivity has the opposite effect, and may be used to offset the negative effect of a higher level of effort.

Landed Value Envelope Curve

We now describe the envelope curve representing landed value as a function of ishing effort, assuming the level of selectivity is optimal for each level of effort. let us name V s (E) the relationship between ishing effort and landed value for a given level of selectivity s. For a given level of effort E, in a situation where no speciic cost is attached to selectivity, the best level of selectivity s*(E) is the one that maximises landed value. As a result, the envelope curve V*(E) is deined as:

( ) ( ) ; * s s V E maxV E E   (14)
i.e., the relationship between effort and landed value, assuming that for each level of effort the selectivity that maximises landed value is adopted (this envelope is represented by the thick line in igure 3).

As noted before, the sign of the partial derivative ∂V/∂s depends on the value of E. If E is lower than E ~ , this derivative is negative, which implies that non-selectivity is optimal (s* = 0). When ishing effort is equal to E ~ , ∂V/∂s is null. It becomes positive for values of E higher than E ~ , which makes full selectivity optimal (s* = 1). Effort E ~ is therefore the switch point where optimal technique changes from non-selectivity to full selectivity. In this model, imperfect selectivity is never optimal, except possibly at the switch point E ~ , where s* is undetermined (when E = E ~ , landed value is independent of the level of s). Combining these results with (10), we get the following expression for the equation of the envelope curve:

     1 0 1 2 1 2 ( ) 1 for ( ) ( ) 1 for ( s )E E s * E s V E p p ae e E V E V E p a e E                    E ~ (15)
The envelope curve is continuous and strictly increasing. When effort grows indeinitely, V*(E) tends asymptotically towards p 2 a. It is differentiable at any point and strictly concave in the neighbourhood of any point, except E

Optimal Combination of Effort and Selectivity

The optimal combination of effort and selectivity in the ishery is the vector (E*, s*) that maximises the ishery rent under biological equilibrium conditions:

π(s,E) = V(s,E) -cE. ( 16
)
With the help of the envelope V * (E), this two-variable optimisation problem may be reduced to the following one-variable problem:

Determine E ≥ 0 such that: [V * (E) -cE] → max.
If effort E* is optimal, it satisies the irst order condition:

* V c. E  (17)
According to this condition, optimal combination of effort and selectivity makes the marginal value of effort on the envelope curve equal to the unit cost of effort. however, due to the shape of the curve representing the relationship between ∂V * /∂E and E, two local optima, both satisfying condition (17), may occur (see igure 4). This will happen if the unit-cost of effort belongs to the interval:

2 1 2 2 1 2 1 , p p a c p a p p a           (18)
the lower and upper bounds of which are equal to the values of ∂V * /∂E for E = E ~ , with the non-selective technique (s = 0), and with the selective technique (s = 1), respectively.

If unit effort cost c belongs to the interval deined by ( 18), the overall optimum may be obtained by comparing rent levels for the two local optima. To this end, let us call "scenario I" the use of a non-selective ishing gear (s = 0), and "scenario II" the use of fully selective gear (s = 1). For each scenario, there is an optimal ishing effort ( , between these two boundaries, such that f(c ~ ) = 0, with f(c) negative for c smaller than c ~, and f(c) positive for c larger than c ~ (igure 5). This result implies that scenario II (selectivity) is more proitable as long as effort cost, c, is smaller than c ~, while scenario I (non-selectivity) becomes more proitable once c has gone beyond c ~. As a result, c ~ is the switch point, in terms of effort cost, between the two techniques.

* * I II E E ),
To illustrate the impact of effort cost on selectivity, let us irst suppose that c < c ~, (igure 4). If the ishery is optimally managed, the technique in use is the selective one (scenario II, with s* = 1). A rise in c, due to an increase in fuel price (for example), will gradually reduce the optimal volume of effort E*, but will not alter the ishing technique as long as the unit cost of ishing effort has not reached the switch point, c ~. once this point is reached, the optimal technique shifts from selectivity to non-selectivity (scenario I, with s* = 0). This change of scenario at point c ~ entails a non-marginal reduction of ishing effort, which is visualised in igure 4 by a "jump" from

E * II (c ~ ) to E * I (c ~ )
. A further increase in c would result anew in a gradual decrease in optimal effort, keeping the nonselective technique in use (as long as c < p 1 + p 2 a, which is the maximum level of effort cost consistent with a proitable activity; if c goes beyond this point, the optimal alternative is to stop ishing).

This very simple model suggests that rising ishing costs are likely to discourage the use of selective ishing gears. However, even within the oversimpliied framework of the model, it is clear that several factors may offset this tendency. one of them is a change in the structure of ex-vessel prices. It can be seen from the Appendix that a decrease in p 1 relative to p 2 will move the curve representing f(c) downward, which, in turn, raises point c ~ where f(c) = 0. As a consequence, the range of costs where selective ishing is optimal widens.

Application to the Nephrops Trawl Fishery of the Bay of Biscay

In this section, we apply our model to a simpliied representation of the French Norway lobster (Nephrops norvegicus) trawl ishery of the Bay of Biscay (ICES VIIIa,b) (igure 6). To this end, we extend the theoretical version that was presented in the former section.

After a short presentation of the case study, we describe the applied version of our model and the scenarios that are run with its help. Finally, we present the simulation results.

Case Study

The Nephrops ishery is one of the most important isheries of the Bay of Biscay. In 2003, its turnover amounted to 33.2 million euros. The leet is composed of some 250 trawlers (mainly twin-trawlers) most of them French 3 and represents approximately 25% of the total number of trawlers operating in the area. In 2003, the average length of these boats was 15 meters, with an engine power of 235 kW, an average age of 19 years, and a yearly average of 200 days at sea (IFREMER 2005). A decrease in the number of vessels targeting Nephrops in the Bay of Biscay has been observed during the last decades, but it has been largely offset by increased ishing eficiency [START_REF] Marchal | Impact of Technological Creep on Fishing Effort and Fishing Mortality, for a Selection of European Fleets[END_REF].

The management of the ishery mainly relies on conservation measures: a total allowable catch (TAC) revised each year, a minimum landing size (MlS, 9 cm corresponding to Nephrops younger than age group 3), and a minimum trawl mesh size (70 mm). In regards to access regulations, a limited entry license system has been enforced since 2005. however, it does not include individual limitations of effort or catches.

The ishery is characterized by high levels of bycatch and discards (ICES 2006). Discards of Nephrops affect mainly small individuals. Respectively 96, 75, and 28% of the Nephrops catches of age groups 1, 2, and 3 (in weight) are discarded, with a minimum 70% rate of mortality [START_REF] Guéguen | Essai de Détermination du Taux de Survie des langoustines hors Taille Rejetées lors des opérations de Pêche Commerciale[END_REF]. Since 2000, ishers have developed a program intended to improve selectivity, with technical assistance from IFREMER. Several selective devices have been tested: square mesh panel to enable hake escapement, Nephrops grid, and mesh size increase. In 2005, the use of a square mesh panel became compulsory. 3 The French quota represents 94% of the annual TAC set by the EU.

Applied Model

Compared to the theoretical model presented in the irst part of this article, the applied model we use to analyse the case study provides a more realistic description of the ishery. An extended bio-economic model of the Bay of Biscay Nephrops ishery is described in [START_REF] Macher | A Cost-beneit Analysis of Improving Trawl Selectivity in the Case of Discards: The Nephrops norvegicus Fishery in the Bay of Biscay[END_REF] The biological component describes the Nephrops stock dynamics. Annual recruitment is assumed to be constant as is mean weight per category. 4 Stock, catches, discards, and catchability parameters were derived from the Nephrops ishery model results at equilibrium using 2004 stock assessment [START_REF] Ices | Report of the Working Group on Nephrops Stocks (WGNEP)[END_REF].

The main features of the technical component may be described as follows. For each category, CPUE is assumed to be proportional to biomass, and catchability is held constant over time. Selectivity scenarios are assumed to modify catchability as described in the theoretical section. On the basis of ishing mortality, the model calculates catches, landings, and discards per category. 5In regards to the economic component of the model, the IFREMER isheries information system provided cost data for the Nephrops leet (IFREMER 2005). Variable costs (fuel, ice, food, etc.) are assumed to be proportional to ishing effort.

Nephrops ex-vessel prices come from auction markets statistics. Annual mean prices are assumed to be exogenous and constant. 6 Small Nephrops obtain a mean ex-vessel price of 7.2 euros/kg, and "large Nephrops" obtain a higher price (13.2 euros/kg).

Nephrops gross return is calculated from landings and prices. Revenues provided by species other than Nephrops are assumed to be constant. net gross return is equal to total gross return minus landing taxes.

According to the so-called "share system," enforced in French small-scale isheries, the net return to be shared is obtained by subtracting so-called "shared costs" from boat net turnover (shared costs are those variable costs that may be isolated on a trip basis, such as fuel or ice).7

Scenarios

The applied model is a simulation model, with exogenous levels of selectivity and effort. The model is it for explicit optimization and also enables us to test a variety of selectivity scenarios and to run simulations for each of them that cover a range of effort levels.

Two scenarios have been designed. Both assume equilibrium conditions (comparative static). The irst scenario, denoted S I , assumes current selectivity. The second scenario, denoted S assumes a higher selectivity such that the catchability for age group 4 and younger age groups is null, while catchability for elder age groups remains unchanged (i.e., at the same level as in S I ). This scenario implies that only individuals belonging to the "large Nephrops" commercial grade are caught.

As in the theoretical model, we assume that the only cost of greater selectivity is the decrease in catches of smaller individuals.

We irst analyse the potential impact of each scenario on the equilibrium gross revenue for various levels of effort. We then test the impact of a range of hypothetical increases in effort cost on the relative proitability of these two scenarios in terms of return to be shared.

Simulations Results

Figure 7 (a,b) summarizes aggregated simulation results, assuming the current level of unit effort cost. Aggregated effort is normalized so that (mE = 1) corresponds to the mean level that was observed over 2001-2003 ("actual effort"). According to our simulations, under the current level of effort (mE=1), scenario S II is optimal. For effort levels below the switch point (E ~ ) depicted in the irst section, scenario S I becomes the most proitable, while for any permanent level of effort beyond that point, scenario S II (where only individuals belonging to the higher commercial grade are caught) is the most proitable (Figure 7a,b). The effort corresponding to the switch point (E

~

) is approximately 7% of the present level. Analyses of the ishery return to be shared (RtbS) provided in igure 8 show that under scenario S I , the actual level of effort is too high, assuming unchanged selectivity, reduction in ishing effort by 40% could raise the RtbS by 50%. Under scenario S II , RtbS is maximised with an effort 60% higher than the current one, and the simulations suggest that it could be 5% higher than it is now. Improving selectivity could be proitable to the ishery without requiring a reduction in ishing effort.

In order to check the impact of effort cost on optimal selectivity, a range of increase in fuel price was simulated (table 2). In this simulation, scenarios S I and S II were run assuming a fuel price multiplier of the average 2001-2003 price level. An increase would naturally impact ishery rent negatively for all levels of effort scenarios. The results show that S I becomes optimal only for very high fuel price increases. Assuming best selectivity, increasing fuel cost tends to reduce the optimal effort level. however, possible fuel cost increases would lead to optimal effort levels still signiicantly beyond the switch point, and, as a result, the best selectivity scenario would still be S II . Associating the rise in fuel price with a change in the relative price of large and small Nephrops could change this situation. As demonstrated in the appendix, such a change modiies the level of unit effort cost that induces a shift in optimal selectivity.

Conclusion

The simple theoretical model presented in the irst part of this article highlights the fact that optimal selectivity depends on ishing effort and, therefore, on prices and costs. One of the conclusions that can be drawn from the model is that, assuming an optimally managed ishery, higher ishing effort costs favour lower selectivity. However, this process is not continuous. As long as unit cost of effort is kept below a critical level, the hierarchy of techniques remains unaltered. It reverses beyond this level, which acts as a switch point. This technical switch goes with a non-marginal reduction in the level of optimal effort. The second part of the article presented an empirical application of the model to the Nephrops ishery of the Bay of Biscay. According to the simulations that were run, the current state of the ishery is far from optimal in terms of effort, as well as selectivity. Keeping selectivity unchanged, rent could be increased by signiicantly decreasing effort, but the increase in rent could be even higher if selectivity was increased, and this would not necessarily require a decrease in global effort.

With respect to present conditions, simulations also suggest that reaching the technical switch point would require a very high increase in effort costs. At irst sight, this result may be regarded as a paradox when considering the current state of the ishery, since the current technique appears to be the least proitable one, even with provision for signiicantly increasing fuel costs. The explanation is twofold. First, the ishery is not optimally managed in terms of effort. Second, costs and beneits of selectivity do not belong to the same category. While the former are purely private, the latter are mainly social; i.e., operate at the scale of the ishery, not at the scale of the individual isher. It is true that this discrepancy is not speciic to selectivity; being rooted in the common-pool nature of the ish stocks, it also concerns ishing effort. However, it is likely to be more acute with selectivity than with effort for two reasons; while decreasing effort normally reduces social costs, it also reduces private costs, although to a lesser degree.

Contrasting with this situation, the beneits of increasing selectivity are in most cases purely social; the probability that an individual isher catches a large ish which he refrained from catching when it was small is close to zero. This problem is often worsened by the fact that under many circumstances current selectivity is non-observable. It makes it relatively easy to circumvent apparent improvements in selectivity (e.g., grids, change in mesh size or shape). This factor may powerfully boost temptations for individual ishers to act as free riders, making non-selective use of apparently selective techniques. A solution to this problem might be a drastic change in the type of gear; e.g., a shift from trawl to pot in the case of the Nephrops ishery. The private costs of such a change, which might seriously decrease in the context of a long-term increase in fuel price, should be balanced by a thorough consideration of its social beneits, which are obviously broader than the ones considered in this article.

let us now express the difference between these two optimal rents as a function of unit effort cost:

* * I II  = f (c). ( 5a
)
This function is deined, continuous, and differentiable over the whole interval delimited by (18). Combining (3a), (4a), and (5a) we get: Moreover, when c is equal to the lower boundary of interval ( 18), this function has the following value:

 

                                 (8a)
which is strictly positive. As a result, there is one and only one value c ~ inside interval (18) such that f(c ~ ) = 0 (see igure 5). For any c lower than c ~, f(c) is negative, which means than selective ishing is more proitable than non-selective ishing. If c becomes higher than c ~, f(c) is positive, which means than non-selective ishing is more proitable than selective ishing.

The position of the curve representing f(c) on igure 5 depends on various parameters, including ex-vessel prices of each age group (p 1 , p 2 ). The easiest way to analyse the inluence of the relative price of landings of these two groups on the relative proitability of the two ishing techniques is to consider the impact of a change in p 1 on f(c), ceteris paribus. The partial derivative of ( * * I II  ) with respect to p 1 is:

  1 1 2 1 3 4 * * I II p . p p a          (9a)
This expression is strictly positive, which implies that a rise in p will increase f(c) for any value of c belonging to interval (18). As a result, the curve representing f(c) in igure 5 will move upward, and point c ~ this curve crosses the horizontal axis (f(c) =

Figure 1 .

 1 Figure 1. Catches of Age Group 1 as a Function of Fishing Effort for Different levels of Selectivity s (arrows outline increasing selectivity)

Figure 2 .

 2 Figure 2. Catches of Age Group 2 as a Function of Fishing Effort for Different levels of Selectivity s (arrow outlines increasing selectivity)

Figure 3 .

 3 Figure 3. landed Value as a Function of Fishing Effort for Different levels of Selectivity (arrows outline increasing selectivity)

~.

  The point of envelope V*(E) where E = E ~ is a sharp point (point G in igure 3), where the left-side derivative of V* with respect to E is lower than the right-side derivative (igure 4).E ~ .

Figure 4 .

 4 Figure 4. Derivative of the Envelope V*(E)

  resulting in a maximum level of rent ( * * I II ). It may be shown that the optimal rent difference between the non-selective scenario and the selective scenario ( f of unit effort cost c (see Appendix). In other words, a high cost of effort favours the non-selective technique with respect to the selective technique. More speciically, as f(c) is negative for the lower boundary of interval (18) and positive for the upper boundary of the same interval, there is an intermediate value, c ~,

Figure 5 .

 5 Figure 5. optimal Rent Difference Between the non-selective and Selective Technique as a Function of Effort Cost

Figure 6 .

 6 Figure 6. nephrops Fishery in ICES Divisions VIIIa,b

Figure 7b .

 7b Figure 7b. Focus on the Switch Point Effort Area of the Equilibrium Gross Revenue (GR) as a Function of Selectivity (SI: non selective scenario, SII: selective scenario) and Fishing Effort for Actual Prices and Costs

Figure 8 .

 8 Figure 8. Equilibrium Fishery Total Return to be Shared (RtbS) as a Function of Selectivity and Fishing Effort for Actual Prices and Costs

  an increasing function of c.

  . Several groups of vessels are considered to differentiate between two ishing strategies (a Northern leet, targeting Nephrops most of the year, and a Southern leet, targeting Nephrops occasionally) and various cost structures linked to vessel length categories. The age-structured biological component describes the dynamics of the nine Nephrops age group. Discards are taken into account. For the sake of the present application, a simpliied version of this model is considered. The main simpliications are that the Nephrops leet is described as a whole and that age-groups are aggregated into two main categories-"small Nephrops," which correspond to age groups 1 to 4 and "large Nephrops," which correspond to age groups 5 to 9-to be better linked to the model described in the former part of the article. The model is comprised of three components: biological, technical, and economic. Inputs are detailed in table 1.

	Table 1
	Parameters of the Applied Model
	Indices
	Small Nephrops Age Groups 1 to 4	1
	larger Nephrops Age Groups 5 to 9 +	2
	Stock Parameters
	Recruitment	354,041
	a	0.7111
	Mean weight (kg/individuals)
	w1	0.0139
	w2	0.0432
	Catches (tonnes)
	Y1	4,056
	Y2	1,719
	Discard Rate (% in weight)
	D1	37
	D2	3
	Catchability
	q1	3.6221E-05
	q2	5.0167E-05
	Price Parameters Euros/kg
	p1	7.4
	p2	13.2
	Fleet Parameters
	nb of vessels	237
	Mean nb of days at sea per vessel	204
	Nephrops gross revenue (millions euros)	41
	Total gross revenue (GR) (millions euros)	85
	landing costs (% of total GR)	5
	Fuel costs (euros/days at sea)	225
	Food costs (euros/days at sea)	31
	Ice costs (euros/days at sea)

Effort multiplier mE Gross Return GR in Euros (millions)

										GR SI			GR SII
		350
	Gross Return GR in Euros (millions)	50 100 150 200 250 300
		0
		0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1	1.1	1.2	1.3	1.4	1.5	1.6	1.7	1.8	1.9	2
								Fishing Effort multiplier mE
		Figure 7a. Equilibrium Gross Revenue (GR) as a Function of Selectivity
	(SI non selective scenario, SII: selective scenario) and Fishing Effort (expressed as a
			multiplication factor of the effort) for Actual Prices and Costs
				.01	0.02	0.03	0.04	0.05 Fishing GR SI 0.06 0.07		0.08	0.09 GR SII 0.1	0.11		0.12	0.13	0.14	0.15

Effort multiplier mE Return to be Shared RtbS in Euros (millions)

	1	0.2	0.3	0.4	0.5	0.6	0.7 Fishing RtbS SI 0.8 0.9	1	1.1	1.2 RtbS SII 1.3 1.4	1.5	1.6	1.7	1.8	1.9	2

Table 2

 2 Impact of a Range of Increase in Fuel Cost on the optimal level of Fishing Effort and optimal Selectivity

	Fuel Cost Multiplier	mE *	Smax	RtbS max
	1	1.628	SII	247
	5	1.017	SII	193
	10	0.738	SII	146
	15	0.573	SII	111
	20	0.456	SII	83
	25	0.364	SII	61
	30	0.290	SII	43
	35	0.226	SII	29
	40	0.172	SII	18
	45	0.123	SII	10
	50	0.054	SI	5
	55	0.038	SI	3
	60	0.023	SI	1

  which is strictly negative. Symmetrically, when c is to the upper boundary of interval (18), the same function has the following value:

		* I		* II	 	2 p a p 1 2 p a 	2	  	1 p p a p 2 1 		ln	1 p p a p 2 1 	  	,	(7a)
	* I	* II			2 p a p 1		1 2	k			1 2 p p a	ln 1	1	1 k / p / p a 2 4		,
		with k=		 3 1		1 p / p a 2	

Possible genetic impacts due to selective harvesting of larger individuals have also been investigated(Jennings and Revill

2007; law 2000). however, these possible long-term effects are not yet fully supported by empirical evidence and will not be considered herein.

The assumption of constant mean weight per age group disregards the potential impact of selectivity on growth parameters[START_REF] Schirripa | Effects of Mortality and Gear Selectivity on the Fish otolith Radius Total length Relation[END_REF].

Some discards are motivated by legal considerations (when individuals under the minimum landing size are caught, they are required to be discarded). Technical and economic reasons also motivate discards, as sorting catches on the deck is a time-consuming and uncomfortable operation, which results in discarding some of the less valuable individuals.

The exogenous character of landing prices is justiied by the fact that the ishery under survey provides only a minor part of the supply on the market for the considered species.

The net return to be shared is split into two parts, determined by a pre-established key, to obtain the crew and owner shares.

Appendix

Optimal Proitability Gap between Non-selective and Selective Fishing as a

Function of Unit Effort Cost

let π I and π II be the ishery rents when ishers use the non-selective technique and the selective technique, respectively. Combining (10) and ( 16) alternatively for (s = 0) and (s = 1), we get:

Maximising the rent for E in each scenario gives:


Christèle Dubourg
email: <christele.dubourg@chu-rennes.fr

Véronique David

Andrea Gropman

Sandra Mercier

Maximilian Muenke

Sylvie Odent

Daniel E Pineda-Alvarez

Erich Roessler

Christ Le Dubourg

Ronique David

Clinical utility gene card for: Holoprosencephaly

Keywords:

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

OMIM of the Gene(s)

# SHH 600725; ZIC2 603073; SIX3 603714; TGIF 602630

# # # #
GLI2 165230; PATCHED-1 601309; DISP1 607502; FOXH1 603621; NODAL 601265

# # # # #

Mutational Spectrum

The following percentages are referred to patients with non-chromosomic, non-syndromic HPE.

-Point mutations and microrearrangements in the four main genes in ~27 of isolated HPE cases (SHH ~12 , ZIC2 ~9 , SIX3 ~5 , % % % % TGIF ~1 ) % -Alterations in minor genes <1 . These minor genes with low mutation frequency rates are tested only in selected cases: for example, % GLI2 is tested when specific abnormalities occur in the development of the pituitary gland, in the context of variable brain and craniofacial anomalies consistent with the broad spectrum of HPE ( ). Pineda-Alvarez et al 2010 (Incidence at birth ( birth prevalence ) or population prevalence): The clinical sensitivity can be dependent on variable factors such as age or family history. In such cases a general statement should be given, even if a quantification can only be made case by case.

By testing the four main genes, mutations are identified in ~27 of isolated HPE cases.

%

-Mutations in SHH are identified in ~12 of propositi (10 30 de novo). The presence of structural brain anomalies in patients with

% -%
SHH mutations is estimated to be ~45 , while the penetrance of any manfestations (including microform HPE) is estimated to be ~90 .

% %

-Mutations in ZIC2 are estimated to occur in up to 9 of propositi (72 de novo). Almost 90 of patients with mutations in ZIC2 % % % have structural brain anomalies, and it is rare that a parent with a mutation will not show clear signs of cognitive impairment.

-Mutations in SIX3 are estimated to occur in up to 5 of propositi (only 14 de novo). About 65 of patients with mutations in SIX3 % % %

have structural brain anomalies, most likely alobar rather than semilobar HPE.

-Of the four genes commonly tested in clinical laboratories, mutations in TGIF are the least common, occurring in ~1 of propositi.

%

The role that alterations in TGIF play are not well understood.

Statistical analysis of combined results showed that propositi with structural brain anomalies who have either alobar or semilobar HPE are more likely to have a ZIC2 mutation, whereas SHH mutations seem to be responsible for most microform HPE ( ).

Solomon et al 2010

In a research context, further analysis include subtelomeres exploration using MLPA, and array CGH.

Clinical Specificity (proportion of negative tests if the disease is not present)

The clinical specificity can be dependent on variable factors such as age or family history. In such cases a general statement should be given, even if a quantification can only be made case by case.

Close to 100 for SHH, ZIC2 and SIX3 alterations, if the biological meaning of variation has been ascertained by robust functional % analyses.

The role of TGIF is less clear.

Positive clinical predictive value

(life time risk to develop the disease if the test is positive).

Recent data point to a complex pattern of inheritance combining multiple interacting genetic and environmental factors (Solomon et al

). Therefore, because of this multihit hypothesis, not all carriers of a single deleterious mutation manifest clinically detectable 2010 symptoms. The identified mutation may be not sufficient to generate HPE, another event like an alteration in another gene (not yet identified) or an environmental factor being necessary.

Negative clinical predictive value

(Probability not to develop the disease if the test is negative).

Assume an increased risk based on family history for a non-affected person. Allelic and locus heterogeneity may need to be considered.

Index case in that family had been tested: Close to 100 if the biological meaning of the identified family variation has been ascertained by robust functional analyses. % Index case in that family had not been tested: Not resolved.

Clinical Utility (Differential) diagnosis: The tested person is clinically affected

(To be answered if in 1.10 A was marked)

" "

Can a diagnosis be made other than through a genetic test?

No. □ (continue with 3.1.4) Yes, □ clinically.

□

imaging.

□ endoscopy.

□ biochemistry.

□ electrophysiology.

□ other (please describe): □ Describe the burden of alternative diagnostic methods to the patient

Brain magnetic resonance (MRI) imaging is essential for diagnosing holoprosencephaly. Modern high resolution MRI allows detailed analysis of the cortical, white matter and deep gray structural anomalies in HPE and leds to better classification of types of HPE (Hahn and ). Barnes, 2010 In prenatal, ultrasound can detect central nervous system and facail abnormalities of severe HPE as early as the first trimester, but is less sensitive for detection of milder forms of HPE, such as lobar HPE. Fetal MRI provide better characterization of brain malformations, but only later in the third trimester of pregnancy (

).

Mercier et al 2010

How is the cost effectiveness of alternative diagnostic methods to be judged? Will disease management be influenced by the result of a genetic test?

No. □ Yes. □ Therapy (please describe)

Depending on clinical symptoms : cerebrospinal fluid shunt for treatment of hydrocephalus; antiepileptic medication; physical therapy, bracing and orthopedic surgery for motor impairment, anticholinergic drugs for dystonia; gastrostomy for oromotor dysfunction; tracheostomy for treatment of upper airway obstruction due to facial anomalies; H2 blockers or proton-pump inhibitors for gastrointestinal problems; modifying of the environment for hypothalamic dysfunction; hormones ( ).

Levey et al 2010 Prognosis (please describe)

Higher mortality correlates with several factors, including the severity of brain malformation, the severity of facial malformation, the presence of a multiple congenital anomaly syndrome, and the presence of chromosomal abnormalities. Survival is associated with the severity of brain malformation (short for alobar, intermediate for semilobar and the best for lobar HPE and mild forms)(

).

Levey et al 2010 Management (please describe)

The results of genetic tests will influence genetic counselling by permitting prenatal diagnosis.

" "

Predictive Setting: The tested person is clinically unaffected but carries an increased risk based on family history (To be answered if in 1.10 B was marked)

" "

Will the result of a genetic test influence lifestyle and prevention?

If the test result is (please describe) positive

If the test result is (please describe) negative

Which options in view of lifestyle and prevention does a person at-risk have if no genetic test has been done (please describe)?

Genetic risk assessment in family members of a diseased person (To be answered if in 1.10 C was marked)

Does a positive genetic test result in the index patient enable a prenatal diagnostic?

Yes, but molecular prenatal diagnosis should be offered in addition to fetal imaging which takes precedence for interpretation of the " "

results.

If applicable, further consequences of testing

Please assume that the result of a genetic test has no immediate medical consequences. Is there any evidence that a genetic test is nevertheless useful for the patient or his/her relatives? (Please describe)

For the parents, the result conveys clarity about an eventual cause of the disease. In case of an identified alteration, heterozygote tests in relatives and prenatal diagnosis in pregnancies at risk can be offered as a consequence.

Ackowledgements:

  (Denaturing High Performance Liquid Chromatography) or HRM (High Resolution DNA Melting) with confirmation by sequencing, or direct bi-directional sequencing -Search for microrearrangements: MLPA (Multiplex Ligation-dependent Probe Amplification) with SALSA Kit P187 Holoprosencephaly (MRC-Holland, Amsterdam, Netherlands) or FISH (Fluorescence In Situ Hybridization) Analytical Validation Parallel analysis of positive and negative controls, depending on analytical method Estimated Frequency of the Disease 2 6

  of a genetic test resolve the genetic situation in that family? No Can a genetic test in the index patient save genetic or other tests in family members? No Does a positive genetic test result in the index patient enable a predictive test in a family member? No Prenatal diagnosis (To be answered if in 1.10 D was marked) " "

prevalence in the ethnic group of investigated person

  Ethnic variations in birth prevalence rates could occur in HPE, but the available data are not convincing. Higher rates were generally observed among less favored minorities probably because of a lower prenatal detection rate of HPE, and consequently less terminations of

	Analytical Sensitivity	"		"
					genotype or disease
	1:10 16000 live births; 1:250 conceptuses (proportion of positive tests if the genotype is present)	present	absent
	pos. neg. D-HPLC and HRM : >95 for heterozygous variants test %	A C	B D
	A: true positives, B: false positives, C: false negatives, D: true negatives
	: A/(A C), + Bi-directional sequencing : close to 100% : D/(D B), sensitivity specificity + pos. predict. value	: A/(A B), +	neg. predict. value	: D/(C D) +
	pregnancy ( MLPA : not yet validated Orioli and Castilla, 2010	).
	Diagnostic Setting Analytical Specificity
	Yes. No. (proportion of negative tests if the genotype is not present)
	A. (Differential) diagnosis	□ □
	B. Predictive Testing D-HPLC and HRM : >95 for heterozygous variants □ □ %
	C. Risk assessment in Relatives □ □
	D. Prenatal Bi-directional sequencing : close to 100% □ □
	Comment: Prenatal diagnosis is based primarily on fetal imaging, but molecular prenatal diagnosis can be performed if a mutation or " " MLPA : not yet validated
	a microrearrangement has been previously identified in a proband. Interpretations of molecular diagnosis must be given with caution, given the lack of strict genotype-phenotype correlation, and should be offered in addition to fetal imaging, using ultrasound followed by Clinical Sensitivity
	fetal RMI. (proportion of positive tests if the disease is present)
	Test characteristics

-

If applicable,

  . Hahn JS , Barnes PD . Neuroimaging advances in holoprosencephaly: refining the spectrum of the midline malformation . Am J Med Genet C Sem Med Genet . 2010 ; Jan 26 154C : 120 -132 2 . Levey EB , Stashinko E , Clegg NJ , Delgado MR . Management of children with holoprosencephaly . Am J Med Genet C Sem Med Genet . 2010 ; Jan 26 154C : 183 -190 3 . Mercier S , Dubourg C , Belleguic M , Pasquier L , Loget P , Lucas J , Bendavid C , Odent S . Genetic counseling and molecular prenatal diagnosis of holoprosencephaly " " (HPE) . Am J Med Genet C Sem Med Genet . 2010 ; Jan 26 154C : 191 -196 4 . Orioli IM , Castilla EE . Epidemiology of Holoprosencephaly: Prevalence and Risk Factors . Am J Med Genet C Sem Med Genet . 2010 ; Jan 26 154C : 13 -21 5 . Pineda-Alvarez DE , Dubourg C , David V , Roessler E , Muenke M . Current recommendations for the molecular evaluation of newly diagnosed holoprosencephaly patients . Am J Med Genet C Sem Med Genet . 2010 ; Jan 26 154C : 93 -101 6 . Solomon BD , Mercier S , V lez é JI , Pineda-Alvarez DE , Wyllie A , Zhou N , Dubourg C , David V , Odent S , Roessler E , Muenke M . Analysis of genotype-phenotype correlations in human holoprosencephaly . Am J Med Genet C Sem Med Genet . 2010 ; Jan 26 154C : 133 -141

	This work was supported by EuroGentest, an EU-FP6 supported NoE, contract number 512148 (EuroGentest Unit 3: Clinical genetics,

" community genetics and public health , Workpackage 3.2) "

References:

1