Paediatric and Perinatal Eptdonwlogy 1993,7,45040

Trends in Down’s syndrome prevalence in California, 1983-1988

Judith A. Hahn and Gary M . Shaw California Birth Defects Monitoring Program, Emeryoille, California

Summary. Trends in the prevalence of Down’s syndrome in livebirths were examined in California from 1983 to 1988, a time period when prenatal screening and subsequent termination of Down’s syndrome pregnancies among women under 35 years of age was increasing. In the population-based ascertainment areas of the California Birth Defects Monitoring Program (CBDMP), 1058 infants with Down‘s syndrome were identified, giving a crude prevalence of 1.03 (95% confidence interval [CI]: 0.97,l.W). The risk ratio for 1988 compared with 1983, after adjusting for maternal age and other confounders, was 0.98 (95% CI: 0.78,1.23), show- ing that there was no change in livebirth prevalence over this period. The data were also examined for mothers under 35 years of age to determine whether increasing use of prenatal saeening leading to termination masked an increase in prevalence. A modest increase was seen.

Introduction

Down’s syndrome (Ds) is among the most prevalent, serious birth defects in California. The prevalence is known to increase with maternal age, but little is known about the aetiology of Down’s syndrome. Epidemiologists traditionally examine trends in disease prevalence as a tool for identdymg clues that may suggest the involvement of environmental and other risk factors that are changing concurrently with the disease prevalence. Over the past 20 years, several studies have examined temporal prevalence trends in Down’s syndrome. The prevalence was found to be increasing in 10 studiesl-10 which examined trend after adjusting for maternal age or within maternal age groups. Three of these stUdies13e6 postu- lated that the increasing trend may have been due to increasing ascertainment over the time period examined. One study” found the ageadjusted Down’s syndrome

Address for correspondence: Judith Hahn, Department of Epidemiology and Biostatistics, Box 1347, University of California, San Francisco CA 94143-1347, USA.

450

Down’s syndrome prevalence 451

prevalence from 1964 to 1970 to be decreasing. Several studies found no change in the prevalence of Down’s syndrome*2-22 after adjusting for maternal age or by examining trend within maternal age groups.

We examined trends in the livebirth prevalence of Down’s syndrome in Cali- fornia in the 1980s as an important step in the process of idenhfymg risk factors which might cause the chromosomal mutation to occur. A multivariate model was used to adjust for maternal age and other factors that might affect the trend. We also examined our data for evidence that an increasing trend in the prevalence of Down’s syndrome pregnancies was not observable due to a concurrent increase in the use of prenatal screening tests, followed by termination of Down’s syndrome pregnancies in mothers under 35 years of age.

Methods 2

The CBDMP is a population-based birth defects registry. The registry uses active surveillance to idenhfy all children up to age 1 year born within specified ascertain- ment areas with major structural malformations.= Abstract forms that contain diagnostic and demographic information are completed for each case. Cases are linked to California Vital Statistics data so that additional demographic variables on the birth certificate may be examined.

In order to examine Down’s syndrome cases, all abstracts of liveborn babies with the International Classification of Diseasesz4 (ICD) code of 758.0 were identi- fied. A child with a positive cytogenetics report of trisomy 21 was considered a case. A dysmorphologist reviewed the abstracts of the remaining potential cases, and considered a child a case if the clinical characteristics described were consistent with Down’s syndrome.

The overall prevalence of Down’s syndrome and the prevalences for each year from 1983 to 1988 were calculated. Poisson regression was used to determine if prevalence was changing over the time period. For these analyses, time was grouped in $month intervals, with time period equal to 1 corresponding to a birth in the interval 1 January to 31 March 1983, and time period equal to 24 correspond- ing to a birth in the interval 1 October to 31 December 1988. This grouping was chosen instead of oneyear intervals, or at the other extreme, actual birth dates, because it provided statistical power to detect small changes in prevalence over time. The simplest Poisson model was:

(1 1

It was assumed that the number of cases observed was a Poisson distributed random variable, with the mean of the distribution a function of time period. A coefficient beta (p) of the time period variable greater than zero meant the preva- lence was increasing with time. Models were also fitted involving other variables in

log (risk for DS) = a + p* (time period)

452

addition to time period, and for these models the mean was assumed to be a function of time period and the variables in the model. The regression models were fitted using maximum likelihood estimation.

Maternal age, known to be associated with the prevalence of Down’s syndrome, the distribution of which may change over time in a population, was considered a confounder. Polynomial and log transformations of maternal age were examined for ages between 14 and 45. A polynomial term was included if the P-value from the likelihood ratio test was less than 0.05. The first model fit was:

].A. Hahn and G.M. Shaw

log (risk for DS) = a + P* (time period) + yl* (maternal age) (2)

The term yz* (maternal age)2 was added to the next model, and y3* (maternal age)3 to the next, until an additional polynomial term did not sipficantly reduce the deviance. A model with maternal age plus the natural log of maternal age was also considered, and the deviance of this model was compared with that of the quad- ratic model, which had the same number of degrees of freedom.

Other variables obtained from the birth certificate, such as: paternal age; ma- ternal race; child’s sex; season of birth; prenatal care in the first 4 months of gestation (yes/no); and parity, were also examined to determine if their additional presence in a multivariate model affected the trend in Down’s syndrome. Counties were added successively to the ascertainment areas of the registry during 1983 to 1988. Therefore, a data collection area variable was created to identrfy the groups of counties that joined the registry‘s ascertainment areas yearly, and was also exam- ined in the model. Cases which could not be linked to a birth certificate and observations with certain variables missing were excluded from the regression analyses.

All analyses were performed with time period and the function of maternal age as determined above in the regression model. Each continuous variable was first examined to determine if a quadratic or log transformation was appropriate. Interactions of variables with time period and confounding were examined. A P-value of less than 0.10 in a likelihood ratio test was considered evidence for statistical interaction. A variable was considered a potential confounder if p, the coefficient of time period, changed by more than 15% when the variable was added to the model. Such a criterion flags more potential confounders when the regression coefficient is small. This process was desirable because small confounding effects that could change the direction of the bend would be detected if the trend was small, while only magnitude and not direction of trend would be affected by confounding when the trend was large.

Adjusting risk ratios for 1988 versus 1983, defined as:

Prevalencet for mid-1988 Prevalencet for mid-1983

Risk ratiot for 1988 vs. 1983 = (3)

(t Adjusted for Variables in the regression model)

Down's syndrome prevalence 453

were calculated using the final regression model. This was estimated using do'P, where 20 is the time period difference between mid-1988 and mid-1983.

The effect of increasing prenatal diagnosis on the prevalence of Down's syn- drome livebirths to mothers under 35 years of age was also studied. The maternal serum alpha fetoprotein ( A F P ) test is a blood test that came into use in California as a screening test in the middle of 1986, affecting births beginning in late 1986.= A low level of AFP in the motheis blood is an indication of possible Down's syndrome.26 An amniocentesis must follow a positive screening test to determine conclusively if the fetus has Down's syndrome. California A F P utilisation rates, detection rates, and termination percentages were used to calculate the predicted number of children that would have been born with Down's syndrome, but were not, due to prenatal detection and termination. We see that:

# of observed Down syndrome births = (ql* ( 1 4 + ql* (l-r)*s + q2* (l-t)*r's)*D (4)

where: D = number of Down's syndrome pregnancies in the population in question;

s = % of the population undergoing prenatal screening; r = probability of Down's syndrome is detected by AFP test; t = probability of termination if Down's syn- drome is detected; p1 = 1-q, =probability of elective abortion, spontaneous abor- tion, or stillbirth; and p z = 1 -q2 = probability of spontaneous abortion or stillbirth.

#thatdonothnn #that have

pronatal scrooning y-l-n\ # Down's syndrome # Down's syndrome

#not born

I born

not detected

# not # born born

detected

# decide to caqy to !erm terminate pregnancy

# decldo to

1 x would 8 Dom's # not # nothm born born bnnbom pmmtd Ti%r

Figure 1. Number of Down's syndrome births and births prevented in mothers under age 35.

454 ] .A. Hahn and G.M. Shaw

These probabilities are illustrated in Figure 1. The predicted number of Down‘s syndrome livebirths prevented as a result of prenatal detection is:

q2*t*r*s*D (5) which equals

(# Down’s syndrome births) *t*Ps*q2

(6) (I-Ps) *q* + ( I 4 *Ps*q2

If p , - p 2 is small, then equation (6) is approximately equal to

(# DS births) *t*Ps. (7) 1 - t*Ps

By adding this number into the data set, we examined whether there would have been an increase over time in Down’s syndrome prevalence if these pregnancies had continued to term. Several values of s were used because prenatal screening utilisation changed over the time period. The population was limited to preg- nancies of mothers under 35 years of age.

Results In the period 1983-1988,1058 liveborn infants with Down’s syndrome were ascer- tained, with 979 (93%) confirmed by positive cytogenetics. There were 1028636 liveborn children in the registry data collection areas for the same time period. The crude prevalence of Down’s syndrome was 1.03 per thousand (95% CI: 0.97,1.09). The prevalences by year of birth were 0.87 (95% CI: 0.67, 1.131, 0.98, (0.80,

1 .o

o-6 t OA LJ I I I I I

1983 1984 1985 1986 1987 1988 Y o u

Figure 2 Down’s syndrome in California 1983-1988.

Down’s syndrome prevalence 455

1.21), 0.94 (0.76,1.15), 1.11 (0.96,1.23, 1.01 (0.90,1.15) and 1.07(0.96,1.19), for the 6 consecutive years studied (Fig. 2). The prevalence (per lo00 livebirths) of Down’s syndrome in this California population was comparable to prevalences observed in Atlanta (0.93, elsewhere in the USA (0.82), and in Canada ( l . l l ) . I 3 Additionally, the predicted prevalence of Down’s syndrome in California, calculated from the observed maternal age speafic risks for British Columbia,’4 was similar to the observed live birth prevalence in that province (1.15 vs. 1.05 per lo00 livebirths).

The coefficient for the time period in the simplest model, equation (11, was 0.008 (standard error (SE): 0.005). The regression line is plotted in Figure 2. The resulting risk ratio for 1988 versus 1983, using equation (31, was 1.17 (95% CI: 0.96, 1.42), indicating no sigxuficant trend.

Seventeen cases which could not be linked to birth certificates and two cases which lacked maternal age information were excluded from all further analyses. Down’s syndrome prevalence rose steeply with maternal age starting around the age of 35. Maternal age was best included in the model as a quadratic.

The continuous variables parity and paternal age were examined with time period, m a t e d age, and maternal age squared in the model, and no polynomial or log transformation of these variables significantly improved the fit. No interactions of time period with parity, paternal age, maternal race, child‘s sex, season of birth, early prenatal care, or data collection area were found to be sigruficant at the 0.10 level. Only parity and data collection area changed the coefficient of time period by more than 15% and were considered confounders. The final multivariate model was:

log (risk ~ O T Down’s syndrome) = a + (time period) + y,* (maternal age) + y2* (maternal ugeI2 + 6* (purity) + ci (8)

where: ci = effect of data collection area i; i = 1,. . . ,6; and = 0. The parameter estimates from the regression are shown in Table 1. The risk ratio

Table 1. Parameter estimates for final multivariate trend model

Parameter

Intercept Period Maternal age Maternal age2

Data collection area 2 Data collection area 3 Data collection area 4 Data collection area 5 Data collection area 6

Parity

Estimate

-5.2152 -0.001 1 -0.2149 O.Oo50 0.0741 0.1080 0.3145 0.1166 0.0547 0.2323

Standard Error

0.4218 0.0058 0.0267 O.ooo4 0.0187 0.1014 0.2500 0.0851 0.1070 0.1700

456

for 1988 versus 1983 was 0.98 (95% CI: 0.78,1.23), showing no trend in the livebirth prevalence of Down’s syndrome after adjustment for confounders.

Among women under 35 years of age, 762 liveborn Down’s syndrome babies were born from 1983 to 1988, with crude prevalence of 0.82 per loo0 (95% CI: 0.76, 0.88). The coefficient for the time period variable, using the unadjusted trend model, equation (11, fit to the data for maternal age less than 35 only, was 0.004 (SE: 0.006). The coefficient p for time period changed by only 6% with the addition of maternal age as a quadratic to the model. Therefore maternal age was not con- sidered a confounder at the younger ages and the analyses of the effects of prenatal screening were carried out using the unadjusted trend model, equation (1). The risk ratio for 1988 versus 1983 among mothers under 35 years of age was 1.09 (95% CI: 0.87, 1.37).

Numbers from several sources were used in obtaining the predicted number of Down‘s syndrome births that may have been prevented as a result of prenatal detection and subsequen; pregnancy termination. The number of tests at the California AFP Screening Program given to women under 35 years of age ranged from 171000 in 1986 to 233000 in 1989 (L. Lustig, personal communication). California livebirths, plus fetal deaths to women under 35, ranged from 442 OOO in 1986 to WOO0 in 1988.27-29 It was assumed that the increases in AFP tests and in livebirths and fetal deaths were linear from the end of 1986 to the end of 1988. The resulting utilisation of the A F P test ranged from 37% for tests in the middle of 1986 (affecting births at the end of 1986) to 46% for tests in the middle of 1988 (affecting births at the end of 1988). These utilisation values were used for s in equation (7), the percentage of the population undergoing prenatal screening. The value used for r, the probability that Down‘s syndrome is detected by an AFF test, was 0.21. This number was based on the results of a study of the California AFP Screening Program.25 The probability that the mother would choose to terminate the preg- nancy if Down’s syndrome was detected ( t ) was taken to be 0.8, based on a California study of 79 women with Down’s syndrome fetuses.30

Using equation (7), we estimated that 37 Down’s syndrome pregnancies that would have been Down’s syndrome livebirths were terminated from late 1986 to 1988. The prevalence resulting from adding these 37 to the observed cases was 0.86 per thousand (95% CI: 0.80,0.92). The coefficient for time period with these data was 0.010 (SE: 0.006). The resulting risk ratio for 1988 versus 1983 was 1.21 (95% CI: 0.97,1.51), suggesting a modest increase in prevalence in mothers under 35 years of age. Figure 3 illustrates the prevalence over time of Down’s syndrome in observed livebirth cases and in observed cases plus the 37 prevented births.

J.A. Hahn and G.M. Shaw

Discussion

We endeavoured to determine if there was a trend in the prevalence of Down’s syndrome, because trends in prevalence that are concurrent with environmental

Down’s syndrome prevalence 457

0.4 ’ I I I I I

1sw - 1984 1SS 1986 1987 1988

Y 8 U

Figure 3. Down’s syndrome in mothers under age 35: observed prevalence; 0 observed prevalence, 37 added; - predicted prevalence; ...... predicted prevalence, 37 added.

changes could give important aetiological clues. Several previous studies of trends in Down’s syndrome prevalence have shown increases or no trend. The large data set gave us good ability to detect a change in prevalence if one existed. Ad- ditionally, because our data set included births in the late 1980s, we had the ability to examine the effects on prevalence of the advent and subsequent increasing utilisation of prenatal screening leading to increasing numbers of terminations among mothers under 35 years of age.

The results showed that after adjusting for variables that might affect the trend, there has been no change in the livebirth prevalence in Down’s syndrome in California from 1983 to 1988. While there was no statistically sigruficant trend, the risk ratio of 1.21 suggested there may have been an increase in prevalence between 1983 and 1988 in mothers under 35 years of age when 37 infants that might have been born with Down’s syndrome were added to the analysis.

Inconsistent or incomplete ascertainment is one possible explanation for the lack of a statistically sigruficant trend. However, data collection was camed out in a standard fashion at hospitals and genetics centres from 1983 to 1988, so it is believed that ascertainment has been constant. Additionally, completeness of case ascertain- ment at the CBDMP has been shown to be 97%.3’

The analysis of trend in mothers under 35 was dependent on the values used in equation (7). The risk ratio for 1988 compared with 1983 prevalence was actually lower than 1.21 if the rate (t) for detecting Down’s syndrome using the AFT’ test is followed by amniocentesis was lower than 0.21. It is also possible that the risk ratio for 1988 compared with 1983 was actually higher than 1.21. This would be true if the

458

rate ( T ) for detecting Down’s syndrome was higher than 0.21, if prenatal screening utilisa tion was under-reported, or if the percentage of women choosing tennin- ation after detecting a Down’s syndrome fetus was actually higher than 80%. However, changes in these parameter values caused only small changes in the risk ratio. For example, if the rate of termination was 0.95, then the risk ratio for 1988 compared with 1983 would have increased only to 1.23.

Violations in the assumption p1 = pz that the probability of elective abortion was small did not affect the risk estimate greatly. For example, if the percentage of Down’s syndrome fetuses that miscarry was 29%, as estimated by and the additional proportion terminated by elective abortion was 15%, then the risk ratio for 1988 compared with 1983 in mothers under35 would be 1.23 (95% CI: 0.98,1.53).

The effect of prenatal diagnosis and termination utilisation among mothers over 35 was not examined. Amniocentesis has been recommended routinely for mothers of 35 and over since the 1970s. AFP tests are not advised in the place of amniocentesis for women’ of 35 and over, so there was no reason to suspect a significant increase in prenatal diagnosis utilisation in the 1980s. As prenatal diagnosis use for this group has been fairly constant in the time period that was studied, and the minority (27% in our study) of mothers of Down‘s syndrome babies are over 35 years of age, it was felt that prenatal diagnostic use in older women would not affect the trend in the prevalence of liveborn babies.

In summary, we saw no significant change in the overall prevalence of Down’s syndrome in California for 1983 to 1988. A modest increase in Down’s syndrome prevalence in women under 35 years of age was seen when we accounted for terminations that may have occurred after detection of Down’s syndrome.

].A. Hahn and G.M. Shaw

Acknowledgements

The author wishes to thank Dr Jane Schulman for support and advice at the studfs inception and Dr Cynthia Curry for case review.

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