Improving Data Timeliness Through Provisional Vital Statistics

Today’s Presentations

• Provisional Vital Statistics: Performance and PotentialMark Flotow, Acting Division Chief, Illinois Center for Health StatisticsIllinois Department of Public Health

• Michigan’s Experience in Producing Provisional Infant Mortality RatesGlenn Copeland, State Registrar & Director, Div. of Vital Records & Health Statistics Michigan Department of Community Health

• Questions/Discussion

Provisional Vital Statistics:Performance and Potential

Mark FlotowIllinois Center for Health Statistics

5 August 2014On behalf of NAPHSIS

Emerging Electronic Systems

- Goodbye to paper-based systems- Hello to near real-time, Web-based vital certificate intake systems

→ a decade of building among the states → national coordination of efforts → legal angle helps the public health angle → births (95+%), death (80%), fetal death

(45+%), marriage and divorce (coming)

Output Harvest (so far . . .)- Timeliness: originators do data entry; record-level edits; simultaneous record access; process monitoring and “preliminary records”; electronic querying

- Improvements (both quality and flexibility): GIS-ready; meta-data; variable sorting; aggregate data error checks; near real-time surveillance; custom products; linkages; populating other databases; Provisional vital Statistics (PvS)

Integration ofProvisional vital Statistics (PvS)- vital statisticians have used PvS for decades to monitor data quality (but often well after the fact)- new generation of vital records intake/output systems allows for “surveillance-level” PvS for data quality- an increasing number of PvS products are being shared with PH agency programs- building experience: “noise” vs. “notion”

(More) Integration ofProvisional vital Statistics (PvS)

- on Web sites and via other public distribution- PvS timeliness versus accuracy: when does it matter (most)?- managing expectations: what any user should be aware of when using PvS . . . the caveats- custom products for targeted audiences- “pure surveillance” versus “value added” PvS

Concept Topic:PvS as Surveillance vs. Estimate

Pros of “pure surveillance” . . .- quick, easy, “as is, right now” system extracts- very timely- useful for experienced users or programs

Cons of “pure surveillance” . . .- very much caveat-dependent; “user beware”- can be misleading when irregularities occur- is it useful for the average user, beyond satisfying

curiosity, or as a “clarion call” data are out?

Concept Topic (continued):PvS as Surveillance vs. Estimate

Pros of “estimate or projection” . . .- incorporates experience or “value added”- ease of comparison for user- can be periodically revised

Cons of “estimate or projection” . . .- can be complicated to systematically produce- becomes more problematic for small populations- projection as forecast; varying elements of doubt

Concept Topic (continued):PvS as Surveillance vs. Estimate

Discussion Points:-Surveillance vs. Projection is not a dichotomy but

includes a spectrum of in-between possibilities- tailoring products to specific users/audience

segmentation/which measures to which programs- how often to update? “Latest and greatest” only?- data quality – sharing the experience

PvS 3-5 Years From Now- routine use of PvS as an integral part of VR/VS production (i.e., a best practice)- enhanced PH surveillance (e.g., maternal mortality)- matched PvS products with PH purpose/use- PvS will cover mainly the latest year or two, as the

production of final vital statistics also has timeliness improvements (more of

More/Better/Faster)- “demise & rebirth” of PvS → less surveillance and all jurisdictions participating (for both birth and deaths) more estimates/projections on the public side of dissemination

PvS 3-5 Years From Now (cont.)- PvS will cover more variables, not just the most popular ones (mainly due to broadening quality control)- state PvS will (continue to) enhance NVSS PvS- possibilities not yet realized; potentials not yet tapped . . .- (PvS and) VS will continue to be “real data” as 100%

sample and as “ground truthing” (vs. “big data”)

Provisional Infant Death DataRecent Experiences and Accomplishments

in Michigan

Glenn Copeland, State Registrar and Director Division for Vital Records and Health StatisticsMichigan Department of Community Health

Michigan’s Status Quo

• Files finalized in June• Statistics developed and released in August• Timeliness took several hits• Vital records forms revisions• Debugging new birth and death systems• Automated systems initially trapped data

• Files and data release pushed into fall• 9 months from year end• Birth cohort infant death files delayed as well

• trailed out to 20 months from birth year.

Impetus for Change

• Governor’s score cards• Using national estimates based on projections• Strong interest in more timely data• Key health priority

• Infant mortality reduction/disparity reduction

• CoIIN• HRSA funds to support comprehensive efforts• Builds on multiple public and private investments • Expects timely data on progress• MCH program sees as key opportunity

• Addresses program goals and compliments Michigan approach

What does timely data look like?• Minimal needs • Rapid estimates of infant mortality

• numbers and rates• Data by county/local health department• Need information by race/ethnicity

• Additional information• Information on birth characteristics

• Birth weight, gestational age, prenatal care, maternal age• Requires linked birth and death files

• Cause of death information

Existing Barriers

• Delays associated with paper filings• Inability to extract death data from new EDR• Delays birth/death matching• Slows location of infant deaths

• Lack of timely interstate exchange• STEVE implementation is pending

• Problems with cause of death• Assignment errors and delays through NCHS

How to get from Here to There

• Birth system is very reliable and timely• Files nearly complete within 60 days• Data very clean • Lacking only out of state resident deliveries

• SUID Surveillance• Already manually locating all infant deaths

• EDR continues to accelerate data• Reducing paper filings

• Trained nosologist on staff

Approach

• Leverage birth data• Estimate out of state resident delivery numbers

• Use prior year(s) data• Get denominator data in 70 days by county

• Use brute force on infant deaths• Route infant deaths for manual coding/keying

• Capture subset of death data to meet specific needs• Add manually coded cause of death

• Estimate out of state resident deaths • Use prior year(s) data

Status

• State/County infant mortality rates for 2013• Released in April 2014

• Preparing for quarterly release of fresh data• Data would be out at 90 day intervals• Plan to provide 12 month cumulative figures

• Plan to provide more detail at 6 months• State and county level data• Birth characteristics for the death cohort• Death characteristics data

• Age at death numbers and rates• Rates by birth weight/prematurity• Cause of death – focus on key conditions

Issues

• Some effort involved• Pay back is prompt feedback to program staff

• Need to solve data exports from EDR• Extract preprocessed data

• Anticipate resolution to NCHS cause coding• Need to leverage automation• Matching of births to deaths

• STEVE implementation• Seamless feeds of STEVE data into EBC/EDR

Summary• You don’t need a final file • If you can just focus

• Isolate what pieces of information are critical• Don’t need to wait on the last variable in the last record

• You can provide decision support much sooner

• Leverage what you have in place as best you can• Introduces error and a grain of salt• Much more valuable to be close than to be precise

• Potential for greater efficiencies are there• Improvements in the pipe to make this very practical