Accessing and using statistical information from Ohio’s VS system OPHA Vital Statistics Conference October 12, 2011 John Paulson Center for Public Health Statistics & Informatics Ohio Department of Health

Ohio Vital Statistics Rich source of information on vital events. Rich source of information on vital events. Provides important statistical measures used in public health. Provides important statistical measures used in public health.

Ohio Vital Statistics: Births Newborn medical and demographic information Newborn medical and demographic information Mom’s demographics Mom’s demographics Prenatal care, delivery, and after delivery information Prenatal care, delivery, and after delivery information Pregnancy Risk factors Pregnancy Risk factors Selected characteristics of the Father Selected characteristics of the Father

Ohio Vital Statistics: Selected Birth Data Birth date Birth date Birth location Birth location Prenatal care info Prenatal care info Type of birth Type of birth Race, ethnicity Race, ethnicity Education Education Abnormal conditions of the newborn Abnormal conditions of the newborn Hospital Reg. Number Hospital Reg. Number Place of birth Place of birth — Medical Risk factors — Method of delivery — Mom’s age, race, ethnicity — Gestation — Gender — Birth weight — Father’s age, race, ethnicity — Mom’s marital status 4

415,000 vital events per year Births

2011 Ohio occurrence births and deaths available for analysis as of 10/08/2011 Data are getting to us rapidly Deaths lag a bit behind births

Ohio occurrence births by month of birth ( final, as of 10/08/2011) Annual birthing cycle Decline in Ohio births

2010 and 2011 Ohio occurrence births Approximately 2,000 fewer births Jan-Aug in 2011

Births per 1,000 women, by age Ohio, <15 Nearly 50% decline! Putting birth events in a population perspective

Diabetes association with NICU admission and heavy birth weight, Ohio, 2009 Percent Singleton births Cross-tabulation of one variable against another

Ohio births occurring in weeks 36-38, and same without indication for induced delivery All week deliveries Data provided to Ohio Perinatal Quality Collaborative

Prenatal care timing component data: Missing data in first prenatal visit and menses, 2010 and 2011 Effort to minimize missing data values

House Bill 197 Hospital Compare website Three indicators are calculated from birth certificate file Three indicators are calculated from birth certificate file –Antenatal steroids –CS among low risk women –Appropriate level of care of very low birth weight babies Presented on the public Hospital Compare website. Presented on the public Hospital Compare website. Started data collection from hospitals in October, 2009 for the items that are not BC derived Started data collection from hospitals in October, 2009 for the items that are not BC derived First display of data went public Jan 1, 2010 First 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 % 1.0 Number visits % 4.0 Mom height/weight % 20.0 WIC status % 15.5 Prev. pregnancies % 7.0 Summary statistics Total missing values in this hospital: 5566 Total missing percentage in this hospital: Missing values per birth in this hospital: 1.26 Rank: 1=most missing 122=least missing: 5

Ohio Vital Statistics: Deaths Decedent demographic information Decedent demographic information Decedent medical information Decedent medical information Decedent birth location information Decedent birth location information Decedent armed forces information Decedent armed forces information Decedent burial information Decedent burial information

Ohio Vital Statistics: Selected Death Data Death date Death date Birth date Birth date Injury type and date Injury type and date Race, Hispanic ethnicity Race, Hispanic ethnicity Age, gender Age, gender Place of death Place of death Marital status Marital status Education Education Place of residence Place of residence Place of occurrence Place of occurrence Cause of death Cause of death Place of birth Place of birth Branch of service Branch of service Disposition type Disposition type Certifier, type Certifier, type Injury place Injury place Hospital of death Hospital of death Filing date Filing date

Sleep related deaths by location of infant at time of death Child Fatality Review annual report, Ohio Department of Health

Place of residence for African American infant deaths (dots) drawn over census tract poverty level Hamilton County, Ohio,

Data warehouse overview

Problem Statement: Data are not accessible Data are not accessible Data are not timely Data are not timely Business Intelligence is not available to support public health policy decisions Business Intelligence is not available to support public health policy decisions Key performance indicators (KPI’s) are not available to drive operational efficiencies Key performance indicators (KPI’s) are not available to drive operational efficiencies Data and analytic tools are not transparent and do not support a self service model Data and analytic tools are not transparent and do not support a self service model

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 OPHIW: Maximize timeliness, efficiency, scalability, functionality and user friendliness Maximize timeliness, efficiency, scalability, functionality and user friendliness Provide a “one-stop shop” where ODH data is accessible in a consistent manner Provide a “one-stop shop” where ODH data is accessible in a consistent manner –Both Secure and Public views –Near real time access to preliminary data using a federated model. More timely data for better decision support More timely data for better decision support Include shared analytic and GIS tools Include shared analytic and GIS tools

Business Case: ODH Perspective Manual data processing reduced Manual data processing reduced Enterprise scalability Enterprise scalability Elimination of duplication of effort Elimination of duplication of effort Health transformation/ State agency partnerships Health transformation/ State agency partnerships HIE readiness HIE readiness LHD Perspective Data accessibility Data accessibility Data timeliness Data timeliness Prepopulate grant forms, AFR’s, SART review, etc. Prepopulate grant forms, AFR’s, SART review, etc. “Mashups” of ODH, LHD, and other data and content “Mashups” of ODH, LHD, and other data and content Integrated community profiling Integrated community profiling Shared statistical & GIS analytic platform Shared statistical & GIS analytic 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 Provider Quality & Performance Inspection & Enforcement Vital Statistics Screening & health services Preparedness ODH Data Being Liberated?

10/11/2011 Open Health Data Initiative core activities: liberate data and catalyze innovation 1.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” using 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