Estimating Prevalence of Spina Bifida in the U.S. Using Administrative Databases

Estimating Prevalence of Spina Bifida in the U.S. Using Administrative Databases


Julie Bershadsky, Sandra Pettingell, Sheryl Larson, Jennifer Hall-Lande, and Libby Hallas, University of Minnesota

Catharine Riley, Tiebin Liu, and Jennita Reefhuis, Centers for Disease Control (CDC)

A boy with spina bifida plays in the surf with his father. The boy is using a bright orange wheelchair with fat tires. He is wearing a gold yellow swim shirt. His father is wearing knee-length shorts and a white t-shirt.
Logos for the Centers for Disease Control (CDC) and the Institute on Community Integration, University of Minnesota.

Project Objective

We do not have an estimate of how many people (of all ages) are currently living with Spina Bifida in the U.S. We do not know if prevalence differs by subgroups. The intent of the current study was to identify key national data sources that can be used to estimate Spina Bifida prevalence, and then provide an accurate and robust prevalence estimate for Spina Bifida in the U.S. and by sex, age group, and state.

Administrative/Claims Data: Health Insurance

Major types of health insurance in the U.S:

  • Employer-Sponsored Insurance (ESI)2019: 162,265,000* *under 65 years
  • Medicaid2019: 64,077,000 also covered by private insurance: 8,960,000
  • Medicare2019: 58,327,000 also covered by private insurance: 30,916,000also covered by Medicaid: 10,868,000

Source: American Community Survey, 2019.

Our Approach

Health Care Cost Institute (HCCI) Employer-sponsored Insurance Data

  • Over 1 billion commercial claims per year
  • > 55 million commercially insured individuals per year
  • Nationwide coverage - all 50 states and D.C.
  • Billing data – Inpatient, Outpatient, Physician, Pharmacy
  • Data views are statistically deidentified and certified by experts
  • Years available in 2023: 2012-2020
  • 2019 state coverage (as a percent of all ESI): range 6-77%, average 33%, median 30%

ICD-10 Diagnosis Codes

Q05 Spina bifida

Q05.0 Cervical spina bifida with hydrocephalus

Q05.1 Thoracic spina bifida with hydrocephalus

Q05.2 Lumbar spina bifida with hydrocephalus

Q05.3 Sacral spina bifida with hydrocephalus

Q05.4 Unspecified spina bifida with hydrocephalus

Q05.5 Cervical spina bifida without hydrocephalus

Q05.6 Thoracic spina bifida without hydrocephalus

Q05.7 Lumbar spina bifida without hydrocephalus

Q05.8 Sacral spina bifida without hydrocephalus

Q05.9 Spina bifida, unspecified

Q76.0 Spina bifida occulta

HCCI (ESI) Algorithm

HCCI Numerator:

  • Spina Bifida ICD-10 diagnosis codes included Q05 to Q05.9 and Q76.0
  • Spina Bifida (SB): at least 1 (distinct) inpatient SB claim OR at least 2 (distinct) outpatient/physician SB claims in 2017–2019
  • Spina Bifida Occulta (SBO): at least 1 (distinct) inpatient SBO claim OR at least 2 (distinct) outpatient/physician SBO claims in 2017–2019
  • Member throughout 2019 (in membership file at least all 12 months of 2019)
  • Under 65 years
    • N (SB) = 6,116 (with overlap*)
    • N (SBO) = 2,356 (with overlap*)
    • *N (SB and SBO overlap) = 464

HCCI Denominator:

  • Member throughout 2019 (in membership file at least all 12 months of 2019)
  • Under 65 years
    • N = 35,581, 831

Preliminary Findings

HCCI Employer-based Insurance Preliminary Prevalence Estimates*


SB: 1.72 per 10,000

SBO: 0.66 per 10,000


SB prevalence

SBO prevalence

0 to 17



18 to 24



25 to 34



35 to 44



45 to 54



55 to 64




SB prevalence

SBO prevalence







  • SB: range 0.67–2.95 per 10,000
  • SBO: range 0.00 (0.26)–1.93 per 10,000

* excluding 65+

HCCI Employer-based Insurance: Applying Prevalence to the Total ESI Population

  • HCCI covers ~30% of all ESI population (nationally)
  • Applied HCCI-based state prevalence to the state’s ESI population excluding 65+ (by state) to get extrapolated state Ns
    • Used 2019 ACS data
  • Summed extrapolated state Ns together
  • Total extrapolated N in ESI population under 65:
    • SB = 27,527
    • SBO = 10,559

HCCI Confirmatory Analysis

  • Reproduced algorithm and applied it to 2018–2020 data
  • Results very close
  • 2020 membership, with 2018-2020 claim look back, excluding 65+:
    • SB: 1.68 per 10,000
    • SBO: 0.61 per 10,000

Next Steps

  • Replicate work with Medicaid and Medicare data from RESDAC
  • Compare results across all 3 populations

Contact Information

  • Julie Bershadsky, PhD,
  • Sandra Pettingell, PhD,
  • University of Minnesota, Institute on Community Integration, Minneapolis, MN 55455, U.S.

This project is funded through a CDC Cooperative agreement (6NU38 OT000280-04-01) from a sub-award received from the Association of University Centers on Disabilities (AUCD).

The University of Minnesota is an equal opportunity educator and employer. This document is available in alternative formats upon request.

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