Accessing and using Accessing and using statistical information from statistical information from

Ohio’s VS system Ohio’s VS system

OPHA Vital Statistics ConferenceOPHA Vital Statistics ConferenceOctober 12, 2011October 12, 2011

John PaulsonJohn Paulson

Center for Public Health Statistics Center for Public Health Statistics & Informatics& Informatics

Ohio Department of HealthOhio Department of Health

Ohio Vital StatisticsOhio Vital Statistics

• Rich source of information Rich source of information on on vital events.vital events.

• Provides important statistical Provides important statistical measures measures used in public health.used in public health.

Ohio Vital Statistics: BirthsOhio Vital Statistics: Births

• Newborn medical and demographic Newborn medical and demographic informationinformation

• Mom’s demographicsMom’s demographics

• Prenatal care, delivery, and after Prenatal care, delivery, and after delivery informationdelivery information

• Pregnancy Risk factors Pregnancy Risk factors

• Selected characteristics of the Selected characteristics of the FatherFather

Ohio Vital Statistics: Selected Birth Ohio Vital Statistics: Selected Birth DataData• Birth dateBirth date• Birth locationBirth location• Prenatal care infoPrenatal care info• Type of birthType of birth• Race, ethnicityRace, ethnicity• EducationEducation• Abnormal Abnormal

conditions of the conditions of the newbornnewborn

• Hospital Reg. Hospital Reg. NumberNumber

• Place of birthPlace of birth

Medical Risk Medical Risk factorsfactors

Method of deliveryMethod of delivery Mom’s age, race, Mom’s age, race,

ethnicityethnicity Gestation Gestation GenderGender Birth weightBirth weight Father’s age, race, Father’s age, race,

ethnicityethnicity Mom’s marital Mom’s marital

statusstatus44

415,000 vital events per 415,000 vital events per yearyear

Births

2011 Ohio occurrence births and 2011 Ohio occurrence births and deaths available for analysis as of deaths available for analysis as of 10/08/201110/08/2011

Data are getting to us rapidly

Deaths lag a bit behind births

2008-11 Ohio occurrence births by month 2008-11 Ohio occurrence births by month of birth (2008-9 final, 2010-11 as of of birth (2008-9 final, 2010-11 as of 10/08/2011)10/08/2011)

Annual birthing cycle

Decline in Ohio births

2010 and 2011 Ohio occurrence births 2010 and 2011 Ohio occurrence births

Approximately 2,000 fewer births Jan-Aug in 2011

BirthsBirths per 1,000 women, by age per 1,000 women, by ageOhio, 1990-2009Ohio, 1990-2009

<15

Nearly 50% decline!

Putting birth events in a population perspective

Diabetes association with NICU Diabetes association with NICU admission and heavy birth weight, admission and heavy birth weight, Ohio, 2009 Ohio, 2009

Percent

Singleton births

Cross-tabulation of one variable against another

Ohio births occurring in weeks 36-38, Ohio births occurring in weeks 36-38, and same without indication for and same without indication for induced deliveryinduced delivery

All 36-38 week deliveries

Data provided to Ohio Perinatal Quality Collaborative

Prenatal care timing component data:Prenatal care timing component data:Missing data in first prenatal visit and menses, 2010 Missing data in first prenatal visit and menses, 2010 and 2011and 2011

Effort to minimize missing data values

House Bill 197 Hospital Compare websiteHouse Bill 197 Hospital Compare website

• Three indicators are calculated from birth Three indicators are calculated from birth certificate filecertificate file– Antenatal steroidsAntenatal steroids– CS among low risk womenCS among low risk women– Appropriate level of care of very low birth weight Appropriate level of care of very low birth weight

babiesbabies

• Presented on the public Hospital Compare Presented on the public Hospital Compare website. website.

• Started data collection from hospitals in October, Started data collection from hospitals in October, 2009 for the items that are not BC derived2009 for the items that are not BC derived

• First display of data went public Jan 1, 2010First display of data went public Jan 1, 2010

IPHIS Hospital ID: B090 Hospital: Sample hospital

Hospital Volume Statistics

Number of births in IPHIS to date: 4409 Percent of Ohio births in IPHIS: 3.2 Hospital's rank among hospitals: 5

Missing Value Statistics Indicator No. missing Percent missing OH-wide missing % Rank

1st Pren. visit dt 1760 39.9 6.3% 1.0 Number visits 1483 33.6 11.3% 4.0 Mom height/weight 461 10.5 8.2% 20.0 WIC status 49 1.1 1.1% 15.5 Prev. pregnancies 113 2.6 1.0% 7.0 Summary statistics

Total missing values in this hospital: 5566 Total missing percentage in this hospital: 14.00 Missing values per birth in this hospital: 1.26 Rank: 1=most missing 122=least missing: 5

Ohio Vital Statistics: DeathsOhio Vital Statistics: Deaths

• Decedent demographic informationDecedent demographic information

• Decedent medical information Decedent medical information

• Decedent birth location informationDecedent birth location information

• Decedent armed forces informationDecedent armed forces information

• Decedent burial informationDecedent burial information

Ohio Vital Statistics: Selected Death Ohio Vital Statistics: Selected Death DataData• Death dateDeath date

• Birth dateBirth date

• Injury type and Injury type and datedate

• Race, Hispanic Race, Hispanic ethnicityethnicity

• Age, genderAge, gender

• Place of deathPlace of death

• Marital statusMarital status

• EducationEducation

• Place of residencePlace of residence

• Place of Place of occurrenceoccurrence

• Cause of deathCause of death

• Place of birthPlace of birth

• Branch of service Branch of service

• Disposition typeDisposition type

• Certifier, typeCertifier, type

• Injury placeInjury place

• Hospital of deathHospital of death

• Filing date Filing date

Sleep related deaths by location of Sleep related deaths by location of infant at time of deathinfant at time of death

Child Fatality Review annual report, Ohio Department of Health

Place of residence for African American infant deaths Place of residence for African American infant deaths (dots)(dots)drawn over census tract poverty leveldrawn over census tract poverty levelHamilton County, Ohio, 2003-2006Hamilton County, Ohio, 2003-2006

Data warehouse overview

Problem Statement:Problem Statement:

• Data are not accessibleData are not accessible

• Data are not timelyData are not timely

• Business Intelligence is not available to Business Intelligence is not available to support public health policy decisionssupport public health policy decisions

• Key performance indicators (KPI’s) are not Key performance indicators (KPI’s) are not available to drive operational efficienciesavailable to drive operational efficiencies

• Data and analytic tools are not transparent Data and analytic tools are not transparent and do not support a self service modeland do not support a self service model

The Present Landscape: The Present Landscape:

Average Age of Data available:● Public: 5 1/2 years old● Secure: 7 years old

• Represents less than 5% of ODH data• Too labor intensive to update data even annually • Scalability concerns• End-of-life technology

Vision of the OPHIWVision of the OPHIW::• Maximize timeliness, efficiency, scalability, Maximize timeliness, efficiency, scalability,

functionality and user friendlinessfunctionality and user friendliness

• Provide a “one-stop shop” where ODH data Provide a “one-stop shop” where ODH data is accessible in a consistent manneris accessible in a consistent manner– Both Secure and Public viewsBoth Secure and Public views– Near real time access to preliminary data using Near real time access to preliminary data using

a federated model. a federated model.

• More timely data for better decision supportMore timely data for better decision support

• Include shared analytic and GIS toolsInclude shared analytic and GIS tools

Business CaseBusiness Case::

ODH PerspectiveODH Perspective• Manual data processing Manual data processing

reducedreduced• Enterprise scalabilityEnterprise scalability• Elimination of Elimination of

duplication of effortduplication of effort• Health transformation/ Health transformation/

State agency State agency partnershipspartnerships

• HIE readinessHIE readiness

LHD PerspectiveLHD Perspective• Data accessibilityData accessibility• Data timelinessData timeliness• Prepopulate grant forms, Prepopulate grant forms,

AFR’s, SART review, etc.AFR’s, SART review, etc.• ““Mashups” of ODH, LHD, Mashups” of ODH, LHD,

and other data and and other data and contentcontent

• Integrated community Integrated community profilingprofiling

• Shared statistical & GIS Shared statistical & GIS analytic platformanalytic platform

Consumable data = transparency and innovation

What kinds of data can we liberate?

Vital Statistics Examples:•Birth events•Death certificate data•Infant mortality•Mass casualty planning

Surveillance & Registry

Surveillance & Registry

Provider Quality &

Performance

Provider Quality &

Performance

Inspection & Enforcemen

t

Inspection & Enforcemen

t

Vital Statistics

Vital Statistics

Screening & health

services

Screening & health

services

PreparednessPreparedness

ODHData Being Liberated?

10/11/2011

Open Health Data Initiative core activities: liberate data and catalyze innovation1.Publish brand new HHS data for public access – while rigorously protecting privacy and confidentiality

2.Make existing HHS data much more accessible -- “machine-readable,” accessible via application programming interfaces (APIs), free, much easier to find

3.Energetically publicize our data to innovators -- who can use it as raw material to develop applications and services that help improve health and health care

Are we on the right track?

The goal: a self-propelled, open “ecosystem of innovation” The goal: a self-propelled, open “ecosystem of innovation” using data to improve healthusing data to improve health

• Help consumers take control of their health and health care

• Help employers promote health and wellness

• Help care providers deliver better care

• Help journalists promote health information

• Help local leaders make better-informed decisions

• Support all of the above through “data intermediary” services

• And much more

A Rapidly Growing Array of Innovative Products and Services That:

Health-Related Data from

FUELS