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California Birth Defects Monitoring

California Birth Defects Registry Data: Technical Notes

Data Timeframe

Birth defects reported among deliveries during 2018–2020

Geographic Coverage

Maternal residence in the 10-county ascertainment region: Fresno, Kern, Kings, Madera, Merced, Orange, San Diego, San Joaquin, Stanislaus, and Tulare which constitute about 30% of annual births in California.

Case Definition

Although the Program monitors over 200 conditions, many are quite rare. The Birth Defects Data Sheets highlight fourteen birth defects or birth defect groups below. Many are common, serious, and have substantial public health impact.

Cases included in these Birth Defects Data Sheets met the following criteria:

  • Live born, stillborn less than or greater than or equalto 20 weeks of gestational age, or medically terminated
  • Diagnosis made through one year of age; hypospadias made through the age of two.
  • The birth defects diagnosis was in the following categories:
    • Anopthalmia/Micropthalmia
    • Anotia/Microtia
    • Cleft Lip/Cleft Palate
    • Craniosynostosis
    • Esophageal Atresia/Tracheoesophageal Fistula
    • Gastroschisis
    • Congenital Heart Defects
    • Hypospadias
    • Limb Deficiencies
    • Neural Tube Defects
    • Omphalocele
    • Trisomy 13
    • Trisomy 18
    • Trisomy 21

Multiple Birth Defects in the Same Case

Cases with multiple defects are counted separately for each defect (i.e., a child with cleft lip and trisomy 13 will be counted as a case of cleft lip, and again as a case of trisomy 13). Therefore, the defect data do not necessarily represent mutually exclusive cases. It is important to recognize that adding up the number of defects will not yield the number of babies with defects.

Demographic Characteristics

California Birth Defects Monitoring Program (CBDMP) registry cases are linked to live birth certificate data from California Department of Public Health Vital Records**.

Mother’s Age at Delivery

The mother’s age (in years) categories used:

  • 24 and under
  • 25–29
  • 30–34
  • 35–39
  • 40 and over

Mother’s Race an​​​d Ethnicity

The mother’s race and ethnicity classifications used in these data sheets uses CA Vital Statistics reporting standards:

  • American Indian or Alaskan Native/Non-Hispanic:* Alaskan Native, American Indian
  • Asian/Non-Hispanic: Asian Indian, Cambodian, Chinese, Filipino, Hmong, Japanese, Korean, Laotian, Thai, Vietnamese, other Asian origin
  • Black/Non-Hispanic: African-American
  • Hispanic any race: Cuban, Mexican, Puerto Rican, other Hispanic origin
  • Other/Non-Hispanic:* Includes any category that is not Alaskan Native or American Indian, Asian, Black, Hispanic, Native Hawaiian or Pacific Islander, or White
  • Native Hawaiian or Pacific Islander/Non-Hispanic:* Guamanian, Hawaiian, Samoan, other Pacific Islanders
  • White/Non-Hispanic: White

Data Analysis

Small Numbers

Small numbers of cases can create analysis problems, such as unstable rate estimates. In case of a rare defect or areas with a small population, the issue of small numbers can arise. The addition of a single birth defect will make the rate for a particular year appear much higher than usual. The fluctuation over time may not be statistically significant. Since a small change in the number of cases reported can result in a relatively large change in rates, caution should also be used in comparing annual rates for a specific defect.

Birth Prevalence Rate

When examining data, a birth prevalence rate better reflects occurrences in a population than does the number of cases. Birth prevalence was calculated as follows:

(Number of birth defect cases) divided by (total number of live births) multiplied by (10,000).

​Prevalence rates were calculated per 10,000 live births by mother’s age and race/ethnicity.

Confidence Intervals

The birth prevalence rate for a specific defect is the best estimate of the true prevalence. To understand the range of possible values for the true prevalence, we also calculate the 95% confidence interval in the Summary Birth Defect Prevalence Table. From a practical viewpoint, confidence intervals are particularly useful when dealing with small numbers of cases or where the birth defect prevalence for one group are compared with that of other groups. It helps minimize reader concern about prevalence values that appear high or different when in fact it is most likely due to random fluctuation. We calculate 95% confidence interval using Clopper-Pearson Method and SAS software version 9.4.

Limitations

These data are subject to several limitations. First, the registry includes birth defects diagnosed in the first year of life, so birth defects detected after the first birthday and diagnoses that are refined after the first birthday may not be recorded in the registry. Second, due to some high-volume referral facilities being outside of ascertainment counties some patients may be transferred outside of our ascertainment region. Due to this, we perform surveillance activities at some facilities outside ascertainment counties, however some diagnoses that are made outside of an ascertainment county or in facilities that our staff do not access, such as prenatal diagnostic facilities and private physicians' offices, may not be captured. Third, data are collected from medical records and as such are subject to differences in clinical practice. And, finally, we perform ascertainment in a subset of California counties (mentioned above). Although this subset is demographically similar to the state's population as a whole, we are unable to provide statewide population-based data at this time.

*Due to the small number of individuals in these populations, these three groups are often combined to aid analyses.​

**Data sources: Births: 2018–2020, Birth Statistical Master File, California Department of Public Health, Center for Health Statistics and Informatics.

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