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The Maternal Infant Health Assessment (MIHA) survey is an annual statewide representative survey of individuals with a recent live birth in California. MIHA collects self-reported information about maternal and infant experiences before, during, and shortly after pregnancy. Read More

MIHA is led by the Maternal, Child and Adolescent Health Division in the California Department of Public Health (CDPH) in collaboration with: CDPH Women, Infants and Children (WIC) Division and the Center for Health Equity at the University of California, San Francisco (UCSF).

Included below:

• Data Analysis
  
• Subgroup Definitions
  
• Indicator Definitions
  
• MIHA County-Level Data Availability
  
• MIHA Regions of California
  
• Data Annotation and Suppression Criteria
  
• Weighting Methods used in publications of MIHA data
  
  

Data Analysis

The percentage and estimated number of pregnant or birthing people in the population with a given health indicator or characteristic are best estimates of the actual prevalence in the population. The 95% confidence interval (95% CI), comprised of the lower 95% confidence limit (Lower 95% CL) and upper 95% confidence limit (Upper 95% CL), indicates that there is a 95% chance that the range contains the actual prevalence in the population. The state, county, and regional data included in the dashboards use three-year aggregated data for the percent and 95% CI, and the annual estimate shown is a three-year average.

Annotation and Suppression

The relative standard error (RSE) is used to measure the statistical reliability of survey estimates. Estimates that should be interpreted with caution due to low statistical reliability (RSE between 30% and 50%) are noted with an asterisk (*) in the MIHA Data Snapshots or in tooltips in the MCAH Data Dashboards, and with a “1” or “Yes” in the Annotated column of the downloadable data table. 

The percent, 95% CI, and annual population estimate are suppressed when the RSE is greater than 50% or could not be calculated, sample numerator is less than five, or the weight population denominator is less than 100. Suppression is noted with a double dash (--). 

For more details see Data Annotation and Suppression Criteria and Weighting Methods.

Subgroup Definitions

Subgroups are based on self-reported data from the MIHA survey or the birth file, and refer to the most recent birth, or pregnancy for the most recent birth, unless otherwise indicated. Any change to a subgroup or subgroup category is noted in the Change in Definition and/or Comparability column(s). Subgroups and subgroup categories listed here are those that have been used in MIHA data publications since 2010.

Indicator Definitions

Indicators are based on self-reported data from the MIHA survey and refer to the most recent birth, or pregnancy for the most recent birth, unless otherwise indicated. Unless noted, the denominator for each indicator includes all individuals with a live birth. Any change to a survey question or indicator, compared to how it was in a prior year, is noted in the Change in Definition and/or Comparability column(s). Indicators listed here are those that have been used in MIHA data publications since 2010.

• Prior Poor Birth Outcomes
• Health Status Before Pregnancy
• Nutrition and Weight
• Intimate Partner Violence (IPV)
  
• Mental Health
  
• Hardships and Support During Pregnancy
• Substance Use
• Pregnancy Intention and Family Planning
• Infant Sleep
  
• Breastfeeding Intention and Duration
  
• Health Care Utilization
  
• Public/Nutrition Program Participation
  
• Health Insurance Coverage
• Demographics
  

Prior Poor Birth Outcomes

Health Status Before Pregnancy

Nutrition and Weight

Intimate Partner Violence (IPV)

Mental Health

Hardships and Support During Pregnancy

Substance Use

Pregnancy Intention and Family Planning

Infant Sleep

Breastfeeding Intention and Duration

Health Care Utilization

Public/Nutrition Program Participation

Health Insurance Coverage

Demographics

MIHA County-Level Data Availability

For the data years 2013-2021, Maternal and Infant Health Assessment (MIHA) county-level data are available for the top 35 counties with the largest numbers of births. 

MIHA Regions of California

Data Annotation and Suppression Criteria

The current MIHA data suppression criteria require estimates to be suppressed when:

• the sample numerator is less than 5,
• the number of women/birthing individuals in the population of interest (population denominator) is less than 100,
• the relative standard error (RSE) is greater than 50%, or
• a measure has been determined to address a sensitive topic and the prevalence is greater than 80% and the unweighted population divided by the weighted population is greater than 50%.

Additionally, estimates are annotated and users are warned to interpret with caution if the RSE is between 30% and 50%. The RSE is a commonly used measure of reliability, or precision, of survey estimates and is calculated using the following formulas:

For estimates with a prevalence ≤ 50%:  

Standard error ÷ estimate

For estimates with a prevalence > 50%:

Standard error ÷ (1-estimate)

Some MIHA publications using data from 2010-2012 used a previous set of suppression criteria in which estimates were suppressed when the number of events (sample numerator) was less than 10. 

Weighting Methods

Sampling weights are created in MIHA to account for the stratified design, oversampling of specific groups, non-response among the birthing people sampled and non-coverage of women/birthing people who could not be sampled because their births were not in the sampling frame. When the final MIHA sample is weighted each year, it is designed to be representative of all individuals who delivered live-born infants in California during the calendar year in which the survey was conducted and who met other criteria, including those who were California residents, at least 15 years of age, and had a singleton, twin, or triplet birth. Although MIHA data are weighted to the entire birthing population, minus exclusions, the survey is only administered in English and Spanish, and results may not be generalizable to birthing people who speak other languages. The population represented by MIHA is referred to as the “target" population and is defined using the annual birth file, which is the final compilation of California birth data released annually by the Center for Health Statistics and Informatics (CHSI). From 1999 to 2017, this file was the Birth Statistical Master File (BSMF) and starting in 2018, it is the California Comprehensive Master Birth File (CCMBF).

The MIHA survey design allows for oversampling of certain groups, meaning their probabilities of selection were greater than the proportions of births they represented in the state. This ensures that enough respondents participate in the survey to allow for analysis. These oversamples have included American Indian/Alaska Native women (2012-2015), Black women (all years), WIC-eligible women not participating in the WIC program (2010-2012), those with a preterm birth (2016 and later), the 20 counties with the most births (2010-2012), and the 35 counties with the most births (2013 to 2021).

Every MIHA respondent is assigned a survey weight, which indicates the number of similar birthing people in California that they represent. Starting in 2010, this State Weight has consisted of 4 components (see below) calculated within strata. Additional steps have been added in subsequent years to create a Final Weight and improve the ability of the sample to represent the target population. Starting in 2011, raking (see details below) was added to the weighting process to adjust the State Weights to more accurately represent the annual birth file, particularly at the county level. Starting in 2013, trimming of weights (see details below) was implemented to reduce the influence of excessively large survey weights. These methods of raking and trimming continue to be used in all MIHA publications since 2013.

Calculation of the State Weight

The components of the State Weight are as follows: 

Non-Coverage Weight

The non-coverage weight accounts for differences between the frame from which the sample is drawn and the target population to which generalizations are made. The MIHA sample is drawn from birth certificate data for births occurring from February through May of each year, which is referred to as the "sampling frame." In 2020, due to the COVID-19 pandemic, the sampling frame was comprised of births occurring from March through June. Birth certificate data files from which the MIHA sample is drawn are provided in monthly batches by the CHSI. The non-coverage weight accounts for the difference between the number of births in the sampling frame and the number in the calendar year. The non-coverage weight also accounts for changes that might be made to the birth file after the sample is taken (e.g., births may not be in the frame files for sampling if they are reported late, but these late reported births are eventually included in the annual birth file). The non-coverage weight is defined, within stratum  S, as:

Number in the Target Population _S ÷ Number in the Sampling Frame _S

Inverse of Sampling Fraction

The sampling fraction is the probability of selection, or the ratio of the number of birthing people sampled to the number of birthing people in the sampling frame. Therefore, the inverse of the sampling fraction within stratum S is:

Number in the Sampling Frame _S ÷ Number Sampled _S

Non-Response Weight

This weight adjusts for non-response to the survey by birthing people who were sampled. The non-response weight is calculated within stratum S as:

Number Sampled _S ÷ Number of Respondents _S

Post-stratification Weight for Non-response (Propensity Score Adjustment)

The non-response weight described above accounts for non-response on factors used to define the strata (e.g., Black race, term or preterm birth, and county/region of residence). Additional individual-level factors may also predict whether a birthing person is likely to respond to the MIHA survey. Therefore, another adjustment for non-response is calculated to make the MIHA survey more representative of the target population from which the sample is taken. The probability of responding (versus not responding) is calculated using a geographically stratified logistic regression model of all sampled individuals. Variables in the logistic regression model come from the annual birth file and include maternal race/ethnicity, US or foreign birthplace, age, education, reported principal source of delivery payment, total children born alive, month prenatal care began, WIC participation, and term or preterm birth. A predicted probability (p) of being a respondent, or propensity score, is output for every individual sampled. The score is then rescaled, which means that p is multiplied by a constant factor for all respondents, so that the sum of p over all respondents now adds to the number of respondents. Starting in 2014, the post-stratification weight is capped at the 99th percentile of the post-stratification weight for each year.

Formula for State Weight

The State Weight is calculated using the four components defined above:

NON-COVERAGE * INVERSE SAMPLING FRACTION * NON-RESPONSE * POST-STRATIFICATION

Adjustments to Create the Final Weight 

Raking Survey Weights (or Iterative Proportional Fitting)

The State Weight alone produces weighted data that are very close to the data from the annual birth file at the state level and for most counties/regions. However, there are some remaining discrepancies between the weighted MIHA data and the annual birth file within subgroups of birthing individuals and at the county and regional levels. Raking the State Weights produces estimates that are closer to those of the annual birth file for subgroups, and at the county and regional level.

Raking is a process by which the weighted prevalence of a selected variable is aligned with the known prevalence in a target population. In MIHA, the State Weights are raked so that weighted birth certificate variable estimates reflect those of the annual birth file as closely as possible at the level of the respondent's sampling region (county or group of counties).

Raking is conducted over a series of predetermined variables, one at a time, in an iterative process. Raking variables include maternal age, race/ethnicity, nativity, prior cesarean section (2010-2012), low birth weight, preterm birth, prior live births, delivery payer, delivery method, BMI before pregnancy (2013 forward), education (2013 forward), and WIC participation (2017 forward). The weight assigned to each birthing individual who falls in category C of raking variable V is multiplied by a factor of:

Number in the Target Population vc ÷ Weighted Number of MIHA Respondents vc

The first adjustment is made to the State Weight calculated in the previous section. This results in a different weight value, which is adjusted using the next raking variable and the process continues for each variable. After this is done for all desired variables, the data are checked to ensure the percentages for each raking variable are as close as possible to those of the annual birth file within the sampling region or group. If results can be adjusted to be more similar to those of the annual birth file, the process starts again with the first raking variable, using the weight from the previous iteration. After the raking process is complete, the resulting weight is rescaled (i.e., multiplied by a constant factor), so that the sum of the raked weights over all respondents adds to the number of people in the annual birth file who meet MIHA's inclusion criteria in that county/region. 

Trimming Survey Weights

The raked weights are trimmed to reduce the influence of excessively high individual weights. Weights within strata identified as having excessively large weights are trimmed at the third standard deviation (99.73^rd percentile), and weights are constrained to a fixed range of the original State Weight. Raked and trimmed weights are rescaled so that totals reflect county birth totals in the annual birth file.

After raking and trimming, differences between county-level and regional-level MIHA data and the annual birth file are small. Very few of the estimates in the largest  counties are greater than three percentage points different from those in the annual birth file after raking. Differences between MIHA and the target population are sometimes greater in smaller sampling regions than in counties that have more births. ​