In the 1960s, @entity2816 caused @entity4218 in thousands of @entity1 worldwide. The @entity4218 were frequently of the intercalary type. As a result, numerous countries started @entity600 surveillance programs. In 1967, the Centers for Disease Control (CDC) started the Metropolitan Atlanta Congenital Defects Program (MACDP), a population-based surveillance system, to provide early warning against new teratogens. Recent studies have shown that @entity2816 may be beneficial for a range of conditions, including @entity5 and @entity791 , and it may once again become widely available. Here, we examine the ability of MACDP to detect an increase in the birth prevalence of @entity4218 as an early warning of fetal exposure to @entity2816 . We calculated base rates for all @entity4218 , for bilateral nonsyndromic intercalary or @entity22044 , and for all nonsyndromic @entity4218 among Atlanta @entity1 born from 1968 through 1993. We used relative risk estimates from previous studies and a range of pregnancy exposure rates for @entity2816 . We tested the statistical power of MACDP to detect subtle changes in the birth prevalence of these defects using Poisson and cumulative sum (CUSUM) techniques. The base rates for all @entity4218 , for bilateral intercalary or @entity22044 , and for all @entity4218 , were 0.53, 0.035, and 0.022/1,000, and the estimated relative risks were 175, 4,570, and 8,180, respectively. We varied the assumed rate of exposure to @entity2816 from 1/10,000 to 5/100. With a 1/1,000 exposure rate, both Poisson and CUSUM techniques will detect a rate change in @entity4218 in about 6 months of monitoring, and a rate change in bilateral intercalary or @entity22044 in about 12 months of monitoring. When monitoring all @entity4218 , a pregnancy exposure rate of 3.5% or less would go unnoticed by the Poisson method and would take more than 50 years for the CUSUM method to signal an alarm with a 1/1,000 exposure rate. However, for rates of exposure less than 1/1,000, a progressively longer period of time or larger sample are needed to detect a rate change by both methods. Our findings highlight the importance of enlarging the monitored population and correct case classification in @entity600 surveillance.

The return of @entity2816 : are XXXX surveillance systems ready?

@entity1 :: ('9606', 'Species') :: ['infants'] @entity5 :: ('MESH:D009369', 'Disease') :: ['cancer'] @entity2816 :: ('MESH:D013792', 'Chemical') :: ['thalidomide'] @entity4218 :: ('MESH:D017880', 'Disease') :: ['intercalary limb deficiencies', 'limb deficiency', 'intercalary limb deficiency', 'limb deficiencies'] @entity600 :: ('MESH:D000014', 'Disease') :: ['birth defect', 'birth defects'] @entity22044 :: ('MESH:C536311', 'Disease') :: ['preaxial deficiencies'] @entity791 :: ('MESH:D000163', 'Disease') :: ['AIDS']
@entity600 :: (MESH:D000014,Disease) :: ['birth defects']
