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American Journal of Public Health logoLink to American Journal of Public Health
. 2006 Dec;96(12):2139–2144. doi: 10.2105/AJPH.2005.069377

Advances in Medical Technology and Creation of Disparities: The Case of Down Syndrome

Babak Khoshnood 1, Catherine De Vigan 1, Véronique Vodovar 1, Gérard Bréart 1, François Goffinet 1, Béatrice Blondel 1
PMCID: PMC1698165  PMID: 17077393

Abstract

Objectives. We assessed socioeconomic differences in probabilities of prena-tal diagnoses of Down syndrome and continuation of pregnancies after such diagnoses, along with the effects of these differences on disparities in live-birth prevalences of Down syndrome.

Methods. Using population-based data derived from 1433 cases of Down syndrome and 3731 control births, we assessed age-adjusted effects of maternal occupation and geographic origin on prenatal diagnoses, as well as overall and live-birth odds, of Down syndrome.

Results. Maternal occupation and geographic origin had significant effects on the probability of a prenatal diagnosis of Down syndrome and on continuation of pregnancy after such a diagnosis. Women in lower-status occupational categories had higher odds of delivering a live-born infant with Down syndrome. In comparison with women in the highest-status occupational category, the age-adjusted odds ratio for a Down syndrome live birth among women without an occupation was 2.4 (95% confidence interval [CI] = 1.7, 3.3). By contrast, there were no disparities in age-adjusted overall likelihood of Down syndrome.

Conclusions. Socioeconomic differences in use of prenatal testing have created disparities in the live-birth prevalence of Down syndrome. Overall Down syndrome risk does not vary according to socioeconomic status.

Prenatal testing designed to detect congenital malformations has progressed considerably over the past 30 years. Particular advancements have been made in the case of Down syndrome,1,2 the foremost known genetic cause of mental retardation.3 second trimesters. Techniques used in making definitive prenatal diagnoses include amniocentesis and chorionic villus sampling. In addition, a noninvasive method of prenatal diagnosis involving molecular detection of fetal DNA in maternal blood is being developed.4

An extensive literature has documented socioeconomic disparities in use of medical services,5–8 particularly prenatal testing,9–13 in several countries. For example, socioeconomic differences in use of prenatal testing persist in France14,15 despite an active national policy aimed at increasing access to prenatal testing16 and an accompanying reduction in the live-birth prevalence of Down syndrome.17 The mechanisms underlying socioeconomic differences in the use of prenatal testing11,18 are not completely understood. However, disparities may be related more to barriers to access and information than to differences in women’s preferences.15 Moreover, previous data suggest that different socioeconomic factors have distinct and partially independent effects on use of prenatal testing.15

The extent to which documented socioeconomic differences in use of prenatal testing have resulted in disparities in the actual proportions of Down syndrome cases diagnosed prenatally is not known. Population-based data have not been used to evaluate socioeconomic differences in the probability of continuation of pregnancy after a prenatal diagnosis of Down syndrome. Moreover, few population-based studies have assessed the impact of socioeconomic differences in use of prenatal testing on the birth prevalence of Down syndrome.12,13,19

Studies conducted in the United States have relied on vital statistics data, which are likely to underestimate the true birth prevalence of Down syndrome. Furthermore, vital statistics do not include cases of Down syndrome in which mothers opt for pregnancy termination. Hence, they do not allow for a complete assessment of socioeconomic differences in the probability of prenatal diagnoses of Down syndrome or the probability of continuation of pregnancy after such diagnoses.

Using population-based data, we assessed the effects of 2 socioeconomic factors, maternal occupation and geographic origin, on the probability of a prenatal diagnosis of Down syndrome and on continuation of pregnancy after such a diagnosis. Also, we examined the effects of these factors on overall and live-birth odds of Down syndrome. We hypothesized that differences in use of prenatal testing might result in mothers in lower-socioeconomic-status groups having higher age-adjusted odds than women in higher-socioeconomic-status groups of delivering a live-born infant with Down syndrome.

METHODS

We used data from the population-based Paris Registry of Congenital Malformations for the period 1983 to 2002. The registry includes women who resided in Greater Paris (Paris and Petite Couronne) and gave birth or terminated their pregnancy in Parisian maternity units (approximately 38 000 births per year) during that time period.

The registry follows the methodology of the European network of registries for congenital malformations (EUROCAT), and data quality is routinely monitored by both the registry and the EUROCAT central registry. In addition, data collection and storage procedures and data quality are examined on a regular basis by France’s National Committee of Registries. Data are collected from multiple sources, including maternity units, neonatology services, and cytogenetic and pathology services, to allow high levels of case ascertainment.

We conducted 2 sets of analyses. The first set involved cases of Down syndrome only. We examined the effects of maternal occupation and geographic origin on the probability of (1) prenatal diagnoses of Down syndrome and (2) continuation of pregnancy after such diagnoses. We estimated probabilities and age-adjusted odds of prenatal diagnosis in relation to maternal occupation and geographic origin for all cases of Down syndrome included in the registry. We then assessed probabilities and age-adjusted odds of continuation of pregnancy among all cases of Down syndrome that had been diagnosed prenatally.

Second, we used a case–control design to assess the effects of maternal occupation and geographic origin on the odds of a delivery involving Down syndrome. Our working hypothesis was that there are no significant socioeconomic disparities in overall maternal age-adjusted odds of Down syndrome. Hence, any observed disparities in the live-birth odds of a Down syndrome delivery would be those created (“de novo”) as a result of unequal use of prenatal testing. To test this hypothesis, we examined both overall and live-birth odds of Down syndrome in relation to maternal occupation and geographic origin.

Cases were defined as live births, pregnancy terminations, and stillbirths involving Down syndrome (in analyses assessing overall odds) or as live births involving Down syndrome. Several a priori criteria were used in selecting control births from the list of malformations included in the registry. The first requirement was that there be no evidence of the prevalence of the malformation in question being associated with socioeconomic factors (e.g., neural tube defects were not included because their prevalence is known to be associated with socioeconomic factors20). Second, selected anomalies were required not to be subject to prenatal diagnosis on a routine basis (e.g., cases of congenital heart disease and gastrointestinal abnormalities were not included). Finally, malformations selected as controls were required to involve a relatively high frequency of occurrence.

Our initial study population included 1698 cases of Down syndrome (live births, pregnancy terminations, and stillbirths combined) and 4005 controls. The control group comprised the following set of isolated anomalies: congenital dislocation of the hip (n = 1562), cleft palate (n = 166), syndactyly (n = 172), clubfoot (n = 966), angioma (n = 593), congenital abnormalities of the integument (n = 450), and anorectal anomalies (n = 96).

The registry includes information on maternal occupation and geographic origin. Data on paternal occupation are collected, but a considerable proportion of this information is missing. Data on other socioeconomic factors, including education and marital status, are not currently available. It should also be noted that French law prohibits collection of data on religious beliefs and ethnic origin.

We classified maternal occupation using the categories defined by the French National Institute of Statistics and Economic Studies. We used the following categories, which generally represent the order of highest to lowest occupational classifications in France: professional (n = 1246), intermediate (n = 1199), administrative/public service (n = 1199), “other” (n = 363), and none (n = 1244). The “other” group included several subcategories comprising a relatively small number of women: artisan/small business owner, shopkeeper/shop assistant, service worker, skilled worker, and unskilled worker. We classified mother’s geographic origin using 4 categories representing the major groups in France: French (n = 3560), North African (n = 603), other African (n = 320), and other (n = 1007).

Data on occupation were missing for 420 (7.4%) women, and data on geographic origin were missing for 213 (3.7%) women. We found no significant differences in the frequency of missing data for maternal geographic origin between cases and controls (3.7% vs 3.8%). Missing data on maternal occupation were more frequent for cases (14.7%) than for controls (4.3%). In addition, the prenatal diagnosis rate was somewhat higher among women with missing data on occupation (79.0%) than in the overall group of women with complete data on occupation (70.6%). However, when women with missing information on occupation were excluded, the prenatal diagnosis rate for the overall sample did not change appreciably (71.8% vs 70.6%).

Cases with missing data on maternal occupation and geographic origin were excluded from the study population. The final sample included 1433 cases of Down syndrome (461 live births) and 3731 controls (3713 live births). Data on maternal age were missing for 11 (0.2%) cases, which were excluded from the age-adjusted analyses.

Data on prenatal diagnosis were missing for 5 cases (0.3%) of Down syndrome, and information regarding continuation of pregnancy after a prenatal diagnosis was missing for 24 cases (2.4%). These cases were excluded from the analyses of the effects of maternal occupation and geographic origin on prenatal diagnoses of Down syndrome (n = 1428; Table 1 ▶) and continuation of pregnancy after such diagnoses (n = 984).

TABLE 1—

Socioeconomic Differences in Prenatal Diagnoses of Down Syndrome and Continuation of Pregnancy After Prenatal Diagnoses: Paris, France, 1983–2002

Prenatal Diagnosis Continuation of Pregnancy After Prenatal Diagnosis
No.a % (95% CI) No.b % (95% CI)
Occupational categoryc
    Professional 375 84.0 (79.9, 87.6) 312 3.2 (1.5, 5.8)
    Intermediate 330 75.5 (70.4, 80.0) 244 2.9 (1.2, 5.8)
    Administrative, public service 279 64.5 (58.6, 70.1) 172 5.8 (2.8, 10.4)
    Otherd 102 65.7 (55.6, 74.8) 63 7.9 (2.6, 17.6)
    No occupation 342 57.6 (52.2, 62.9) 193 11.4 (7.3, 16.7)
Geographic originc
    France 916 73.9 (70.9, 76.7) 664 2.9 (1.7, 4.4)
    North Africa 165 55.2 (47.2, 62.9) 88 15.9 (9.0, 25.3)
    Other Africa 79 59.5 (47.9, 70.4) 42 21.4 (10.3, 36.8)
    Other 268 72.0 (66.2, 77.3) 190 6.3 (3.3, 10.8)
        All regions 1428 70.6 (68.1, 72.9) 984 5.5 (4.1, 7.1)
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Note. CI = confidence interval.

a Total number of Down syndrome cases.

b Total number of prenatally diagnosed cases of Down syndrome.

cP < .001 (χ2 test of significance) for both prenatal diagnosis and continuation of pregnancy.

d Artisan, small business owner, shopkeeper, shop assistant, service worker, skilled worker, or unskilled worker.

Amounts of missing prenatal diagnosis information did not differ significantly across the maternal occupation and geographic origin categories. In comparison with women in the highest occupational category (the “professional” group; n=3; 1.0%), more women in the intermediate (n=5; 2.0%), administrative/public service (n=8; 4.4%), other (N=4, 6.0%), and no occupation (n=4; 2.0%) categories had missing data on continuation of pregnancy after a prenatal diagnosis of Down syndrome. Also, more women of North African (n=3; 3.3%) and African (n=5; 10.6%) origin than women from France (n=13; 1.9%) had missing data on continuation of pregnancy.

Given increasing frequencies of delayed childbearing among women in higher socioeconomic groups and the strong effect of maternal age on the risk of fetal Down syndrome, adequate adjustment for maternal age is crucial in studies of the relation between socioeconomic factors and overall or live-birth prevalence of Down syndrome. We used 2 alternative strategies of adjusting for maternal age.

First, we used the cumulative sum (cusum) model for binary variables,21 a nonparametric technique designed to assess alternative strategies for adjustment of a continuous variable (e.g., maternal age) in logistic regression models. Second, we used fractional polynomials22 to determine the optimal strategy for adjusting for maternal age. These 2 strategies produced similar results in terms of estimates of the age-adjusted effects of maternal occupation and geographic origin on overall and live-birth odds of Down syndrome. Here we present the results of analyses in which fractional polynomials were used.

RESULTS

Of the 1428 cases of Down syndrome, 1008 (70.6%; 95% confidence interval [CI]= 68.1%, 72.9%) were diagnosed prenatally. In the case of both maternal occupation and geographic origin, there was a substantial socioeconomic gradient in the probability of a pre-natal diagnosis of Down syndrome (Table 1 ▶). Probabilities of prenatal diagnoses were significantly higher among women from more advanced occupational categories than among women in the other categories. Also, probabilities were higher among women of French origin than among women of African origin. Differences in the odds of prenatal diagnoses across occupational categories and between women of North African origin and those of French origin were independently significant and remained so after adjustment for maternal age (Table 2 ▶).

TABLE 2—

Socioeconomic Differences in Odds of Prenatal Diagnoses of Down Syndrome and Continuation of Pregnancy After Prenatal Diagnoses: Paris, France, 1983–2002

Prenatal Diagnosis, Adjusted ORa (95% CI) Continuation of Pregnancy After Prenatal Diagnosis, Adjusted ORa (95% CI)
Occupational categoryb
    Professional 1.0 1.0
    Intermediate 0.7 (0.4, 1.0) 0.8 (0.3, 2.2)
    Administrative, public service 0.4 (0.3, 0.7) 1.2 (0.5, 3.0)
    Otherc 0.3 (0.2, 0.6) 1.5 (0.5, 4.8)
    No occupation 0.3 (0.2, 0.5) 1.7 (0.7, 4.1)
Geographic origind
    France 1.0 1.0
    North Africa 0.5 (0.3, 0.7) 5.0 (2.3, 11.1)
    Other Africa 0.8 (0.5, 1.4) 6.3 (2.5, 16.2)
    Other 1.2 (0.8, 1.7) 1.7 (0.8, 3.8)
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Note. OR = odds ratio; CI = confidence interval.

aAdjusted for maternal age, occupation, and geographic origin. Fractional polynomials were used to adjust for maternal age.

bP < .001 (Wald test of significance) for prenatal diagnosis; P = .54 (Wald test of significance) for continuation of pregnancy.

cArtisan, small business owner, shopkeeper, shop assistant, service worker, skilled worker, or unskilled worker.

dP < .001 (Wald test of significance) for both prenatal diagnosis and continuation of pregnancy.

Among the women in the sample, 5.5% (95% CI = 4.1%, 7.1%) continued their pregnancy after a prenatal diagnosis of Down syndrome (Table 1 ▶). There was a tendency for women from lower occupational categories (vs those from the highest category) and women of African origin (vs those of French origin) to be more likely to continue their pregnancy after such a diagnosis. Differences in the probability of continuation of pregnancy across occupational categories were no longer significant, however, after adjustment for maternal age and geographic origin (Table 2 ▶). By contrast, odds of continuation of pregnancy remained significantly higher among women of African origin after adjustment for maternal age and occupation.

Table 3 ▶ shows the results of the logistic regression analyses assessing socioeconomic differences in overall and live-birth odds of Down syndrome. Overall odds of Down syndrome were reduced among women in lower occupational categories (vs those in the highest category), suggesting a lower overall prevalence for these women. This difference disappeared after adjustment for maternal age. We found no significant differences in odds among women of different geographic origins, suggesting that the overall prevalence of Down syndrome was similar among these women.

TABLE 3—

Results of Case–Control Logistic Regression Analysis of Socioeconomic Differences in Overall and Live-Birth Odds of Down Syndrome: Paris, France, 1983–2002

Total Birthsa Total Birthsa Live Births Live Births
Unadjusted OR (95% CI) Pb Adjusted ORc (95% CI) Pb Unadjusted OR (95% CI) Pb Adjusted ORc (95% CI) Pb
Occupational category .01 .70 <.001 <.001
    Professional 1.0 1.0 1.0 1.0
    Intermediate 0.9 (0.7, 1.1) 1.1 (0.9, 1.3) 1.3 (1.0, 1.9) 1.5 (1.1, 2.1)
    Administrative, public service 0.7 (0.6, 0.9) 1.0 (0.8, 1.2) 1.6 (1.2, 2.2) 1.9 (1.3, 2.6)
    Otherd 0.9 (0.7, 1.2) 1.0 (0.7, 1.4) 2.0 (1.3, 3.1) 2.0 (1.3, 3.1)
    No occupation 0.9 (0.7, 1.0) 1.1 (0.9, 1.4) 2.3 (1.7, 3.1) 2.4 (1.7, 3.3)
Geographic origin .71 .76 <.001 <.001
    France 1.0 1.0 1.0 1.0
    North Africa 1.1 (0.9, 1.4) 0.9 (0.7, 1.2) 2.2 (1.7, 2.8) 1.5 (1.1, 2.0)
    Other Africa 1.0 (0.7, 1.3) 1.1 (0.8, 1.6) 1.7 (1.2, 2.5) 1.3 (0.9, 2.0)
    Other 1.0 (0.9, 1.2) 1.0 (0.9, 1.3) 1.1 (0.9, 1.5) 1.0 (0.7, 1.3)
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Note. OR = odds ratio; CI = confidence interval. Odds ratios refer to odds of a Down syndrome birth (cases) relative to a birth involving one of the following anomalies (controls): congenital dislocation of the hip, cleft palate, syndactyly, clubfoot, angioma, congenital abnormalities of the integument, or anorectal anomalies.

aLive births, pregnancy terminations, and stillbirths.

b Wald tests of significance of the overall effect of maternal occupation or geographic origin.

cAdjusted for maternal age, profession, and geographic origin. Fractional polynomials were used in adjusting for maternal age.

dArtisan, small business owner, shopkeeper, shop assistant, service worker, skilled worker, or unskilled worker.

Conversely, there were substantial socioeconomic differences in the odds of a Down syndrome live birth. Women in lower occupational categories had higher odds of delivering a live-born infant with Down syndrome both before and after adjustment for maternal age and geographic origin. For example, relative to women in the highest occupational category, odds ratios were 1.5 (95% CI = 1.1, 2.1) for women in the intermediate category and 2.4 (95% CI = 1.7, 3.3) for women with no profession after adjustment for maternal age and geographic origin. In addition, women of North African origin had higher odds (OR = 1.5; 95% CI = 1.1, 2.0) of delivering a live-born infant with Down syndrome than women of French origin after adjustment for maternal age and occupation.

DISCUSSION

Our results suggest considerable disparities in prenatal diagnoses of Down syndrome and, therefore, in women’s odds of delivering a live-born infant with this condition. This was particularly the case across maternal occupational groups, with increasingly higher odds of Down syndrome live births among women in lower occupational groups. Women with no occupation were at more than 2-fold risk of delivering a live-born infant with Down syndrome than women in the highest occupational category. These disparities in prenatal diagnoses and live-birth prevalences of Down syndrome persisted in the context of a prena-tal testing policy that has egalitarian intentions and provides reimbursed access to pre-natal testing.16,23

By contrast, we did not find any evidence of socioeconomic differences in the overall odds of Down syndrome after adjustment for maternal age. Together, these results suggest that the increasing use of prenatal testing accompanied by persistent differences in its use has created disparities in the live-birth prevalence of Down syndrome, a congenital anomaly whose overall risk does not seem to vary according to socioeconomic status after socioeconomic differences in frequency of delayed childbearing have been taken into account.24,25

We also found differences in the probability of continuation of pregnancy after a pre-natal diagnosis of Down syndrome among women of different geographic origins, suggesting that women’s own preferences may also contribute to socioeconomic differences in the live-birth prevalence of Down syndrome. Such differences in preferences with respect to prenatal testing and pregnancy termination have been observed in France14,15 and other countries26,27 and, in particular, in a recent US study18 conducted by Kuppermann and colleagues that involved a socioeconomically diverse sample and included a comprehensive evaluation of women’s preferences.

It should be noted, however, that previous data28,29 and the results of our study suggest that by far the majority of women across different socioeconomic groups do not continue their pregnancy after a prenatal diagnosis of Down syndrome. Moreover, differences in use of prenatal testing appear to be for the most part because of barriers to access and information rather than differences in women’s preferences or informed decision-making.15 It is also possible that the apparent differences in preferences are related in part to factors associated with health providers, particularly miscommunication between providers and pregnant women from different cultural backgrounds.27

Limitations

Interpretation of our results is subject to several caveats and limitations. In our case–control analysis of differences in overall and live-birth odds of Down syndrome, we used a selective set of malformations from the Paris registry as controls. The limitations and advantages of the use of such controls in epi-demiological studies of congenital malformations have been discussed elsewhere.30–33 The main issue of concern has been the possibility of selection bias. As mentioned earlier, we sought to minimize such bias by setting a priori criteria for selection of controls.

Our results indicating an absence of socioeconomic differences in age-adjusted overall odds of Down syndrome are consistent with the findings of previous studies.34 In addition, the fact that we found no significant disparities in the maternal age-adjusted overall odds of Down syndrome suggests that any bias related to socioeconomic differences in the malformations used as controls was negligible. That is, such selection bias would need to exactly balance the differences we found regarding live-birth odds of Down syndrome to result in an absence of socioeconomic effects on overall odds, which does not seem very likely. Moreover, the socioeconomic differences we found in odds of live birth were consistent with our findings on disparities in prenatal diagnoses and continuation of pregnancy after such diagnoses.

Missing data on maternal profession were more frequent for Down syndrome cases than for controls. However, we found no significant differences in prenatal diagnosis rates between cases of Down syndrome initially included in the study and those included in the final study population, which excluded cases with missing data on maternal occupation. Also, we are not aware of any a priori reason or empirical evidence suggesting differences in reporting of maternal occupation in cases of Down syndrome vis-à-vis the malformations included here as controls.

Women in the “no occupation” and “other” occupational groups were more likely than women in the remaining occupational groups to have missing data on continuation of pregnancy after a prenatal diagnosis of Down syndrome, and women of African origin were more likely than women of French origin not to have complete information on this variable. However, only a small number of cases involved missing continuation of pregnancy data, and any differences in misclassification because of missing data were unlikely to have had a substantial impact on our estimates.

We examined the effects of only 2 socioeconomic factors, maternal occupation and geographic origin, because data on other socioeconomic variables were not available from the Paris registry. Clearly, women’s socioeconomic status and its possible effects on prenatal testing cannot be comprehensively represented by occupation and geographic origin alone. Indeed, previous studies have shown that other socioeconomic factors also affect use of prenatal testing13–15,35 and that they do so in ways that are, at least to some extent, independent of maternal occupation and geographic origin.

We did not examine time trends in disparities, which merit their own separate analysis. A previous study17 revealed substantial age-specific increases in overall proportions of Down syndrome cases diagnosed prenatally in the Parisian population during the period 1983 through 2000. Trends in prenatal diagnoses showed substantial increases, particularly among younger women, until the early 1990s; thereafter, increases were less pronounced. That study also revealed an overall trend of decreases in live-birth prevalences of Down syndrome despite increases in its overall prevalence as a result of delayed childbearing.

The estimates offered here can be considered “average” effects over the study period. However, because of the much faster rate of increase in prenatal Down syndrome diagnoses in the 1980s and early 1990s compared with the more recent period (after 1996), our estimates reflect much more closely current rates of prenatal diagnosis, particularly among younger women, than 1980s rates. In addition, prenatal Down syndrome diagnosis rates tend to be higher in the Paris registry than in most other European registries.36 Therefore, the available data suggest that the prenatal diagnosis rate reported for our overall study period is fairly representative of current rates in several European countries.

Conclusions

Preferences and cultural values should clearly be considered in evaluations of prena-tal testing policies.37–39 At the same time, socioeconomic differences in prenatal testing that result from barriers to access and information should be addressed. Otherwise, as a result of the increasing use of prenatal testing, a new set of disparities in live-birth prevalences of severe types of congenital anomalies are likely to emerge. Moreover, in situations in which prenatal diagnoses substantially improve outcomes among newborns with congenital anomalies,40–42 disparities in mortality, morbidity, and neurodevelopmental outcomes may begin to be observed.

Our results suggest that the increasing use of prenatal testing, accompanied by persistent socioeconomic differences in its use, has created disparities in the live-birth prevalence of Down syndrome, a congenital malformation whose overall risk does not vary according to socioeconomic status. Thus, socioeconomic differences in the live-birth prevalence of Down syndrome constitute an example of the creation of disparities in health outcomes for which socioeconomic inequalities did not exist initially. Such disparities come about as a result of advances in, and the increasing use of, medical care technology (e.g., prenatal testing), along with socioeconomic differences in its use.8 In the case of congenital anomalies, disparities in prenatal testing imply that families with fewer resources may become disproportionately responsible for the care of infants born with the more severe types of anomalies.

Acknowledgments

The Paris Registry of Congenital Malformations received financial support from INSERM and the Institut de Veille Sanitaire.

We thank the staff of the Paris maternity units for their participation in the collection of data used in this analysis.

Human Participation Protection …No protocol approval was needed for this study.

Peer Reviewed

Contributors…B. Khoshnood, C. De Vigan, and B. Blondel originated the study. B. Khoshnood conducted the statistical analyses and wrote the first draft. C. De Vigan and B. Blondel contributed to the conceptualization of ideas and made suggestions about the required analyses. All of the authors contributed to the interpretation of findings and revisions of the article.

References

  • 1.Wald NJ, Watt HC, Hackshaw AK. Integrated screening for Down’s syndrome on the basis of tests performed during the first and second trimesters. N Engl J Med. 1999;341:461–467. [DOI] [PubMed] [Google Scholar]
  • 2.Wapner R, Thom E, Simpson JL, et al. First-trimester screening for trisomies 21 and 18. N Engl J Med. 2003;349:1405–1413. [DOI] [PubMed] [Google Scholar]
  • 3.Roizen NJ, Patterson D. Down’s syndrome. Lancet. 2003;361:1281–1289. [DOI] [PubMed] [Google Scholar]
  • 4.Lee T, LeShane ES, Messerlian GM, et al. Down syndrome and cell-free fetal DNA in archived maternal serum. Am J Obstet Gynecol. 2002;187:1217–1221. [DOI] [PubMed] [Google Scholar]
  • 5.Hart JT. The inverse care law. Lancet. 1971;1:405–412. [DOI] [PubMed] [Google Scholar]
  • 6.Fiscella K, Franks P, Gold MR, Clancy CM. Inequality in quality: addressing socioeconomic, racial, and ethnic disparities in health care. JAMA. 2000;283:2579–2584. [DOI] [PubMed] [Google Scholar]
  • 7.Gortmaker SL, Wise PH. The first injustice: socioeconomic disparities, health services technology, and infant mortality. Annu Rev Sociol. 1997;23:147–170. [DOI] [PubMed] [Google Scholar]
  • 8.Victora CG, Barros FC, Vaughan JP. The impact of health interventions on inequalities: infant and child health in Brazil. In: Leon D, Walt G, eds. Poverty, Inequality and Health: An International Perspective. New York, NY: Oxford University Press Inc; 2001:125–136.
  • 9.Sokal DC, Byrd JR, Chen AT, Goldberg MF, Oakley GPJ. Prenatal chromosomal diagnosis: racial and geographic variation for older women in Georgia. JAMA. 1980;244:1355–1357. [DOI] [PubMed] [Google Scholar]
  • 10.Halliday J, Lumley J, Watson L. Comparison of women who do and do not have amniocentesis or chorionic villus sampling. Lancet. 1995;345:704–709. [DOI] [PubMed] [Google Scholar]
  • 11.Kuppermann M, Gates E, Washington AE. Racial-ethnic differences in prenatal diagnostic test use and outcomes: preferences, socioeconomics, or patient knowledge? Obstet Gynecol. 1996;87:675–682. [DOI] [PubMed] [Google Scholar]
  • 12.Khoshnood B, Pryde P, Wall S, et al. Ethnic differences in the impact of advanced maternal age on birth prevalence of Down syndrome. Am J Public Health. 2000;90:1778–1781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Khoshnood B, Wall S, Pryde P, Lee KS. Maternal education modifies the age-related increase in the birth prevalence of Down syndrome. Prenat Diagn. 2004; 24:79–82. [DOI] [PubMed] [Google Scholar]
  • 14.Julian-Reynier C, Macquart-Moulin G, Moatti JP, et al. Reasons for women’s non-uptake of amniocentesis. Prenat Diagn. 1994;14:859–864. [DOI] [PubMed] [Google Scholar]
  • 15.Khoshnood B, Blondel B, De Vigan C, Breart G. Socioeconomic barriers to informed decisionmaking regarding maternal serum screening for Down syndrome: results of the French National Perinatal Survey of 1998. Am J Public Health. 2004;94:484–491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ayme S, Morichon N, Goujard J, Nisand I. Prenatal diagnosis in France. Eur J Hum Genet. 1997;5(suppl 1): 26–31. [PubMed] [Google Scholar]
  • 17.Khoshnood B, De Vigan C, Vodovar V, Goujard J, Goffinet F. A population-based evaluation of the impact of antenatal screening for Down’s syndrome in France, 1981–2000. Br J Obstet Gynaecol. 2004;111:485–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Kuppermann M, Nease RF Jr, Gates E et al. How do women of diverse backgrounds value prenatal testing outcomes? Prenat Diagn. 2004;24:424–429. [DOI] [PubMed] [Google Scholar]
  • 19.Bishop J, Huether CA, Torfs C, Lorey F, Deddens J. Epidemiologic study of Down syndrome in a racially diverse California population, 1989–1991. Am J Epidemiol. 1997;145:134–147. [DOI] [PubMed] [Google Scholar]
  • 20.Wasserman CR, Shaw GM, Selvin S, Gould JB, Syme SL. Socioeconomic status, neighborhood social conditions, and neural tube defects. Am J Public Health. 1998;88:1674–1680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Royston P. The use of cusums and other techniques in modelling continuous covariates in logistic regression. Stat Med. 1992;11:1115–1129. [DOI] [PubMed] [Google Scholar]
  • 22.Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28:964–974. [DOI] [PubMed] [Google Scholar]
  • 23.De Vigan C, Vodovar V, Verite V, Dehe S, Goujard J. Current French practices for prenatal diagnosis of trisomy 21: a population-based study in Paris, 1992–97. Prenat Diagn. 1999;19:1113–1118. [PubMed] [Google Scholar]
  • 24.Casterline JB, Lee RD, Foote KA. Fertility in the United States: New Patterns, New Theories. New York, NY: Population Council; 1996.
  • 25.Bréart G. Delayed childbearing. Eur J Obstet Gynecol Reprod Biol. 1997;75:71–73. [DOI] [PubMed] [Google Scholar]
  • 26.Zlotogora J. Parental decisions to abort or continue a pregnancy with an abnormal finding after an invasive prenatal test. Prenat Diagn. 2002;22:1102–1106. [DOI] [PubMed] [Google Scholar]
  • 27.Browner CH, Preloran HM, Casado MC, Bass HN, Walker AP. Genetic counseling gone awry: miscommu-nication between prenatal genetic service providers and Mexican-origin clients. Soc Sci Med. 2003;56:1933–1946. [DOI] [PubMed] [Google Scholar]
  • 28.Pryde PG, Drugan A, Johnson MP, Isada NB, Evans MI. Prenatal diagnosis: choices women make about pursuing testing and acting on abnormal results. Clin Obstet Gynecol. 1993;36:469–509. [PubMed] [Google Scholar]
  • 29.Mansfield C, Hopfer S, Marteau TM. Termination rates after prenatal diagnosis of Down syndrome, spina bifida, anencephaly, and Turner and Klinefelter syndromes: a systematic literature review. Prenat Diagn. 1999;19:808–812. [PubMed] [Google Scholar]
  • 30.Wacholder S, Silverman DT, McLaughlin JK, Mandel JS. Selection of controls in case-control studies: II. Types of controls. Am J Epidemiol. 1992;135:1029–1041. [DOI] [PubMed] [Google Scholar]
  • 31.Hook EB. Normal or affected controls in case-control studies of congenital malformations and other birth defects: reporting bias issues. Epidemiology. 1993; 4:182–184. [DOI] [PubMed] [Google Scholar]
  • 32.Lieff S, Olshan AF, Werler M, Savitz DA, Mitchell AA. Selection bias and the use of controls with malformations in case-control studies of birth defects. Epidemiology. 1999;10:238–241. [PubMed] [Google Scholar]
  • 33.Swan SH, Shaw GM, Schulman J. Reporting and selection bias in case-control studies of congenital malformations. Epidemiology. 1992;3:356–363. [DOI] [PubMed] [Google Scholar]
  • 34.Vrijheid M, Dolk H, Stone D, et al. Socioeconomic inequalities in risk of congenital anomaly. Arch Dis Child. 2000;82:349–352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Khoshnood B, Blondel B, De Vigan C, Bréart G. Effects of maternal age and education on the pattern of prenatal testing: implications for the use of antenatal screening as a solution to the growing number of amniocenteses. Am J Obstet Gynecol. 2003;189:1336–1342. [DOI] [PubMed] [Google Scholar]
  • 36.EUROCAT Working Group. EUROCAT Report 8: Surveillance of Congenital Anomalies in Europe, 1980–1999. Newtownabbey, Northern Ireland: University of Ulster; 2002.
  • 37.Kuppermann M, Feeny D, Gates E, et al. Preferences of women facing a prenatal diagnostic choice: long-term outcomes matter most. Prenat Diagn. 1999; 19:711–716. [PubMed] [Google Scholar]
  • 38.Kuppermann M, Goldberg JD, Nease RF Jr, Washington AE. Who should be offered prenatal diagnosis? The 35-year-old question. Am J Public Health. 1999;89:160–163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Asch A. Prenatal diagnosis and selective abortion: a challenge to practice and policy. Am J Public Health. 1999;89:1649–1657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Bonnet D, Coltri A, Butera G, et al. Detection of transposition of the great arteries in fetuses reduces neonatal morbidity and mortality. Circulation. 1999; 99:916–918. [DOI] [PubMed] [Google Scholar]
  • 41.De Vigan C, Goujard J, Vodovar V, Uzan S. Management of the fetus with a correctable malformation in Paris maternity units: evolution 1985–1994. Fetal Diagn Ther. 1997;12:216–220. [DOI] [PubMed] [Google Scholar]
  • 42.Khoshnood B, De Vigan C, Vodovar V, et al. Trends in prenatal diagnosis, pregnancy termination, and perinatal mortality of newborns with congenital heart disease in France, 1983–2000: a population-based evaluation. Pediatrics. 2005;115:95–101. [DOI] [PubMed] [Google Scholar]

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