About the birth defects & folic acid data
This page provides general information about the birth defects and folic acid data and measures developed by the Minnesota Environmental Public Health Tracking (MN EPHT) Program. For more information about these data, contact MNPH Data Access.
Information on this page:
- The numbers and prevalence rate of select birth defects in Hennepin and Ramsey counties, MN by year, race/ethnicity of mother, and age of mother.
- If a segment of a population is at higher risk for select birth defects.
- The percent of Minnesota mothers who reported they take daily folic acid vitamins during the month before pregnancy by race and Hispanic ethnicity, WIC participant status, education level, and poverty level.
- Disparities in folic acid intake-Minnesota mothers who reported they were less likely to take daily vitamins.
- To inform the public about select birth defects.
- For program planning and evaluation by state and local partners.
- Public health planners use PRAMS data to help understand maternal behaviors and experiences and their relationship to pregnancy outcomes.
- These data cannot tell us what causes birth defects, or factors that lead to changes in birth defect rates.
- MN Tracking birth defects measures are prevalence rates of 12 selected birth defects per 10,000 live births. The incidence of birth defects would be the ideal measure for birth defects, but it cannot be determined as it requires information that is difficult to determine, such as the number of conceptions and the number of cases "lost" through fetal deaths (i.e., miscarriages, terminations).
- MN Tracking birth defects measures are for only Hennepin and Ramsey counties in Minnesota. In 2010, the Minnesota Legislature provided additional funding to Minnesota Department of Health (MDH) to expand birth defects surveillance statewide. Over the next several years, MDH will provide data for other counties, as the data become available.
- How much folate or folic acid Minnesotan women are actually consuming.
- Whether consuming folic acid prevented a birth defect for that particular individual.
Birth defects data:
- The Minnesota Department of Health (MDH) Birth Defects Program uses a multi-source active surveillance methodology with program review of all cases and clinical review of selected cases. The program uses National Birth Defects Prevention Network (NBDPN) guidelines for validating each birth defect diagnosis. About sixty birth conditions are currently tracked in Minnesota, including most of the birth defects recommended by NBDPN and CDC. The program uses CDC/BPA codes to categorize these conditions.The conditions must be diagnosed before one year of age to be entered into the Minnesota Birth Defects Information System (BDIS). Minnesota began active birth defects surveillance on June 1, 2005 in two counties.The program has been expanding throughout the state since 2010. The first calendar year of birth data available is 2006; it includes children born to mothers who resided in Hennepin and Ramsey counties. As the data become available, analysis from larger portions of the state will be possible. Data from Hennepin and Ramsey counties are available for 2006-2010 births, from the 7-county metro area for 2011-2012 births, and for the entire state starting with 2013 births. Because birth defects are rare, it is necessary to combine several birth years to analyze the data effectively. The most recent five birth years avaialble from BDIS ar presented in the tbales and charts. As more data becomes available, analysis from larger portions of the state will be possible.
- The number of live-born infants in the geographic region of interest is from birth certificate data filed with the MDH Office of Vital Records for the same five calendar years. BDIS data are linked to Minnesota birth certificate data to determine maternal race, maternal ethnicity, and maternal county of residence at birth. Race and ethnicity categories for the measures are based on the race and ethnicity of the mother as reported on the birth certificate. Hispanic ethnicity includes anyone indicating they are of Hispanic/Latino descent regardless of race. The other category includes all other races besides white non-Hispanic and Black non-Hispanic. Maternal race unknowns were excluded from the race/ethnicity charts; the chart count totals for race do not equal the total counts by birth defect, since unknown race were excluded from the race/ethnicity charts. Multi-race is addressed through hierarchical selection of maternal race.
Folic Acid data:
- The Pregnancy Risk Assessment Monitoring System (PRAMS) is a surveillance project between the Minnesota Department of Health (MDH) and the Centers for Disease Control and Prevention (CDC). MN PRAMS was established in 2002 to reduce infant death, illness, and low birth weight in Minnesota. PRAMS is a population–based survey designed to collect information on the behaviors and experiences of mothers before, during, and after a pregnancy. http://www.health.state.mn.us/divs/cfh/program/prams/index.cfm
- The MDH Birth Defects Program performs active surveillance, which means trained abstractors review medical records to make sure all reported potential cases meet rigorous case definitions established by both Minnesota and national experts.
- After the birth defects information is reviewed and validated, parents of children in the database are notified by mail of their right to have identifying information removed from the database if they choose ("opt-out"). For families that choose to opt-out, birth defect information is retained in the database, but is not connected to any of the child's or parents' identifying information.
- Only live births are included in BDIS. Minnesota does not include stillbirths or pregnancy terminations in its case ascertainment.
- The Birth Defects Program uses multiple data sources to help ensure that all cases are identified. In addition to the primary case finding source of hospital medical records, they use birth certificates, death certificates, and newborn screening data for case finding.
- Quality control and data evaluation efforts are performed annually, including an assessment of completeness, accuracy and timeliness. Because cases may be abstracted through one year of age and then subsequent quality control analysis must be performed, there is a 2-3 year data reporting lag.
- These data include information about babies born in Minnesota with certain health conditions (Anencephaly, Spina Bifida, Hypoplastic Left Heart Syndrome, Tetralogy of Fallot, Transposition of the Great Arteries, cleft lip with cleft palate, cleft lip without cleft palate, cleft palate without cleft lip, Hypospadias, Gastroschisis, Limb deficiences, Trisomy 21) diagnosed within the first year of life.
- Each month, approximately 200 mothers are selected from the Minnesota Vital Statistics file of birth certificates of babies born in Minnesota during the preceding 2–4 months. Mothers must be Minnesota residents and have delivered a live–born infant. A PRAMS questionnaire is mailed to these mothers with instructions for completing and returning the information. Some surveys are completed by a telephone interview. Minnesota uses the standardized data collection methods developed by CDC.
- The number indicates the total number of birth defects, not the number of people with birth defects.
- To understand the magnitude or what the overall burden is, use the number.
- If there are fewer than 5 cases, MN Tracking and the Birth Defects Program suppress those numbers to preserve data privacy.
- A rate is a ratio between two measures with different units.
- In our analysis, a rate is calculated using the number of events as the numerator (the number of birth defects during a period of time) divided by the number of people at risk as the denominator (the number of live births during the same period of time). This fraction is then multiplied by a constant to make the number more legible. The constant is 10,000 for birth defect measures.
- To understand the probability or what the underlying risk in a population is, use a rate.
- To protect an individual's privacy, rates based on counts under 5 are suppressed.
- Rates have been rounded to the nearest tenth of a percent.
Birth defects data:
- MN Tracking birth defects measures are aggregated using the most recent 5 year grouping. Because birth defect rates are based on only five years of aggregated data, the rates may be suppressed or vary considerably when compared with data from other states that have been collecting birth defects information for a longer time period.
- For births prior to 2011, population-based surveillance is only possible in Hennepin and Ramsey counties. About one-third of all births in the state are among residents of these two counties. In 2010, the Minnesota Legislature provided additional funding to MDH to expand birth defects surveillance statewide. Over the next several years, MDH will provide data for other counties, as the data become available.
- In Minnesota, an "opt-out" clause that allows a parent to remove any personally identifying information on that child from the system makes it impossible to generate population-based measures in areas smaller than counties.
- Residential information is very important when examining environmental exposures that occur before birth. A limitation of the data source is that the place of residence during pregnancy may not be represented by maternal residence at time of birth. Address data at conception would be a more relevant time period for birth defects-related exposure than address data at delivery. Adoption replaces demographic characteristics of the birth mother (including mother's race/ethnicity) on the birth certificate with those of the adoptive mother. Replacement of birth mother address with adoptive mother address further biases the place of residence data element; if known, the Birth Defects Program attempts to retain the birth mother's age, race, ethnicity and county of residence at the time of birth and exclude the adoptive mother's demographic information when an adoption is identified.
Folic Acid data:
- There are some limitations in the PRAMS data: recall bias and non-response bias.
- PRAMS respondents are contacted within 2-6 months after giving birth and questions are asked about behaviors throughout the perinatal period. Due to this long time frame it is possible that the accuracy of the data may be impacted by the mother's ability to recall all of the past events.
- PRAMS surveys are mailed based on address information collected from the birth certificate files and other sources. Surveys are only printed in English and Spanish. Surveys administered by the telephone are only in English and Spanish. Therefore, populations with language barriers may not complete the survey. It is possible that the results in the non-response population could differ from those of the respondents. To compensate, PRAMS includes a non-response weight for each group based on specific demographic information of non-respondents.
- Approximately 200 women are selected each month to participate in the survey. During 2009-2011, the overall response rate ranged from 66-70% (CDC PRAMS requires a minimum of 65% response rate to report data at the national PRAMS level). Response rates for the oversampled groups of U.S.-born African-American and American Indian mothers are lower and could results in potential non-response bias.
Poverty was determined using the federal poverty levels or guidelines issued annually by the U.S. Department of Health and Human Services (HHS). The FPL is often used by federal programs to determine financial eligibility of individuals (based on the total number of persons in a household) for public benefit programs, such as Children's Health Insurance Program, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), and Supplemental Nutrition Assistance Program (SNAP) (formerly Food Stamp Program). The HHS poverty guidelines, or percentage multiples of them (such as 125% FPL, 150% FPL, or 185% FPL), are used as an eligibility criterion by a number of federal programs. For example, WIC uses less than or equal to 185% FPL or receiving Medicaid as an eligibility criterion for women and children to enroll. The poverty level (at 100% FPL) for a four person family was $22,050 in 2009, $22,050 in 2010, and $22,350 in 2011 in the 48 contiguous states (which represents the data in the folic acid and poverty chart). To learn more, go to: HHS Poverty Guidelines.
- PRAMS data was displayed using three categories of poverty. "Poor" includes households whose incomes are within 0-100% of the federal poverty level. "Near-poor" poverty includes households whose incomes are within 101-185% of the federal poverty level and "Not poor" are those with household incomes greater than 185% of the federal poverty level.
- A weighted percent is an adjustment of the crude percent (which is just the count divided by sample size or N) and takes into account variables like sampling design and characteristics of survey respondents (e.g. age, sex, race/ethnicity) to make the percentage generalizable to all Minnesota mothers that had a live birth during that time period. Sample weighting is done so that unbiased population estimates can be calculated using the results of a survey.
- Minnesota PRAMS statistics are based on weighted data. The weights are adjusted to account for sample design, nonresponse patterns, and omissions from the sampling frame. Weighting is necessary to give unbiased estimates.
- For some surveys, it is important to ensure that there are enough members of a certain subgroup in the population so that more reliable estimates can be reported for that group. To do this, members of a specific subgroup of interest are oversampled by selecting more people from this group than would typically be done if everyone in the sample had an equal chance of being selected.
- PRAMS data are essential to implementing Minnesota´s goal of eliminating health disparities. In Minnesota, African American (U.S.–born) and American Indian mothers have poorer birth outcomes than other mothers. Therefore, African American and American Indian mothers are oversampled in order to obtain estimates that are more precise for these maternal populations.