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Using Vital Records for Public Health Surveillance: Some U.S. Examples

Using Vital Records for Public Health Surveillance: Some U.S. Examples. Mark Flotow Illinois Dept of Public Health, Center for Health Statistics NAPHSIS President 4 March 2011 Vital Statistics Summit – Ottawa, Canada. What is Public Health Surveillance?.

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Using Vital Records for Public Health Surveillance: Some U.S. Examples

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  1. Using Vital Records for Public Health Surveillance:Some U.S. Examples Mark Flotow Illinois Dept of Public Health, Center for Health Statistics NAPHSIS President 4 March 2011 Vital Statistics Summit – Ottawa, Canada

  2. What is Public Health Surveillance? • Monitoring emerging infectious diseases • Monitoring well-established infectious diseases • Monitoring chronic disease • Monitoring morbidity and mortality agents/factors • Monitoring health-related behaviors • Monitoring newborns’ outcomes/health • Monitoring quality of health care • Monitoring food, water, drugs, dairy, etc. • Nota bene: monitoring = collection, analysis and sharing of surveillance information • Concept of public health vs. population health

  3. Public Health Surveillance: How • Sentinel event reporting and monitoring • Screening (e.g., lab testing) • Registries (e.g., for cancer or vital events) • Administrative records (e.g., hospital discharge data) • Population surveys (e.g., PRAMS) • Direct reporting to public health programs (e.g., PHIN) • Direct inspection (e.g., health care facilities) • Anecdotal reporting (e.g., for rare events) • PRAMS = pregnancy risk assessment and monitoring system (CDC program) • PHIN = public health information network

  4. Public Health Surveillance: When • Real time (or close to it; PHIN, inspections) • Within a week (e.g., fact of death; VR) • Within a month (e.g., cause of death; VR) • Within in a year (e.g., behaviors; BRFSS, PRAMS) • More than a year (e.g., chronic diseases, cancer registry) • VR = vital records or vital records system • PRAMS = pregnancy risk assessment and monitoring system (state implemented CDC program) • PHIN = public health information network • BRFSS = behavioral risk factor surveillance system (state implemented CDC program)

  5. Public Health Surveillance: Who • Epidemiologists, lab staff • Physicians, nurses • Surveyors and their clients • Google (e.g., seasonal influenza activity by locality) • Statisticians, researchers • Registrars • Nota bene: many of these surveillance activities can occur at a variety of geographic levels or locations – national, state/province/territory, county/minor civil district, city, local health department, university, hospital, clinic, physician office, etc.

  6. Vital Records as Part of Public Health Surveillance • What does Vital Records (VR) have to offer regarding Public Health (PH) surveillance? • 100% sample • Good data quality • Universal standards • Timely collection • Electronic capture (for the most part, and increasing) • Long time series of data • Trending towards GIS-compatible geocoding • Universal recognition and use

  7. Vital Records as Part of Public Health Surveillance • Advantages/Characteristics of Electronic VR Systems: Collection • 100% sample needs point-of-collection input (Web-based) and dedication to training • Good data quality is everyone’s business: intake system, VR unit, tabulators, data users • Universal standards start at the national, if not international, level • Timely collection includes timely feedback and potentially fewer follow-back queries

  8. Vital Records as Part of Public Health Surveillance • Advantages/Characteristics of Electronic VR Systems: Collection (cont.) • GIS-compatible geocoding relies less on geo-political boundaries for accuracy • Easy output and sharing, especially before a periodic file is complete • Sharing of system designs and standards

  9. Vital Records as Part of Public Health Surveillance • Advantages of Electronic VR Systems: Tabulation/Analysis • Many variables for cross-tabulation (i.e., all data are entered from certificates) • Aggregate data quality can be done regularly as files are being built • Readily available time series for data quality and analysis • GIS geocoding allows for any geographic delineation, not just for geo-political bounded entities • Small-area analysis, isobars, what-ifs, etc.

  10. Vital Records as Part of Public Health Surveillance • Advantages of Electronic VR Systems: Sharing/Linking • Web-based data query systems: everyone can be a researcher (?) • Data warehousing (actual or virtual) • EVVE (electronic verification of vital events) • STEVE (state and territorial exchange of vital events) • Combining with other survey or surveillance data

  11. Vital Records as Part of Public Health Surveillance • Examples of VR Surveillance: Indirect • Chronic disease (premature mortality rates) • Infant mortality (maternal and child health), including SIDS • Life expectancy • Certain program-specific infectious diseases (e.g., influenza, diabetes) • Nota bene: here “indirect” means at an aggregate level

  12. Vital Records as Part of Public Health Surveillance • Examples of VR Surveillance: Direct • Maternal mortality, case investigation • Adverse pregnancy outcomes reporting system (APORS) • Early hearing detection and intervention (EHDI) program • TB surveillance • NCI SEER (surveillance epidemiology and end results) • Census of fatal occupational injuries • in conjunction with NEDSS (disease names in VR C-o-D) • PRAMS • Nota bene: here “direct” means at an individual record level

  13. What is PRAMS? • Pregnancy Risk Assessment Monitoring System (PRAMS) • CDC program administered at the state level • Ongoing population-based surveillance system • Collects state-specific data • Self-reported data on maternal behaviors and experiences • Survey data merged with birth certificate data

  14. PRAMS Objectives • To promote the collection of population-based data of high scientific quality • To conduct comprehensive analyses • To translate results into useable information for public health action • To assist states in building capacity for collecting, analyzing, and translating data

  15. PRAMS Background • Established in 1987 as part of an Infant Health Initiative • Congressional funding to CDC to establish state-based programs • Reduce maternal and infant morbidity and mortality • Maternal and infant health programs • Health policies • Maternal behaviors

  16. WA ME VT MN OR NY MI RI NYC NE NJ OH IL UT WV MD CO NC NM OK AR SC MS AL GA LA TX AK FL HI PRAMS Participation, 2010 MA SDT WI WY PA DE VA MO TN Prior to 2006 Funded in 2006 Note: PRAMS represents approximately 75% of all US live births

  17. PRAMS Organizational Structure • Collaboration between CDC and funded states/sites • CDC provides: • Overall project direction • Standard methodology and model protocol • Questionnaire development coordination • Technical support & data management • Base funding • States/sites provide: • State-specific modifications to methods and questionnaire • Additional resources • Access to birth certificate variables

  18. PRAMS Questionnaire • 14-pages, booklet format (self-administered) • Telephone version (interviewer-administered) • English and Spanish versions • Core questions common to all states • State specific questions • Bank of standard pre-tested questions that states can select from • State developed questions

  19. Selected Topics in PRAMS • Unintended pregnancy • Cigarette smoking and alcohol use • Physical abuse • Multi-vitamin use • Breast-feeding • Prenatal care • HIV counseling and testing • Medicaid and WIC participation • Infant health and care • Infant sleep position

  20. PRAMS Population of Interest Women who delivered a live-born infant • Residents of state • Birth within the calendar year of data collection The birth certificate file (monthly) is the starting point for determining the sampling frame for the PRAMS survey.

  21. How the Birth Certificate is Used to Implement PRAMS • Identifying the PRAMS Sample • Data Collection • Data Weighting (both monthly and annually) • Data Linkage • Providing demographics and other information for analysis

  22. The Value of Data Linkage in PRAMS • Reduces respondent burden • Improves accuracy (better detection and measurement) • Reduces follow-up costs • Recent CDC PRAMS application package invited data linkage activities as examples of enhanced projects (beyond the basics)

  23. Exclusions to the Sampling Frame(e.g., how VRs are used) • Stillbirths, fetal deaths, & induced abortions • Out-of-state occurrences • In-state births to nonresidents of the state • Records missing mother’s name • Records processed too late (> 6 months from birth) • Multiple gestations rules; adoptions; surrogate births; mothers under a certain age

  24. PONDER • Menu-driven, web-based analysis request system • Accessible via Inside PRAMS • Includes all core and computed variables and more common standard variables • Data from 2000 to present • New years of data are available as soon as weighted

  25. CPONDER • Public use version of PONDER www.cdc.gov/PRAMS/CPONDER.htm • Includes prevalence and trend data for selected core and standard indicators • Best source for cross-state comparisons and sharing data

  26. CDC PRAMS Websites • CDC, Division of Reproductive Health, PRAMS page: http://www.cdc.gov/prams/ • CPONDER PRAMS data query system; Access from PRAMS website at http://www.cdc.gov/prams/CPONDER.htm

  27. IL PRAMS: Smoking During Pregnancy 1998-2007

  28. IL PRAMS: Breastfeeding Initiation and Duration 1998-2007

  29. Illinois PRAMS/VR Surveillance: Data to Action Paper: “Delivery hospital practices associated with breastfeeding continuation and exclusivity in Illinois” by Amanda Bennett, Dr. Deborah Rosenberg and Dr. Myrtis Sullivan (all University of Illinois – Chicago) Data: 2000-2006 PRAMS data (n=11,987) Results: determined which delivery hospital practices were associated with increased breastfeeding and which were not (see next slide)

  30. Illinois PRAMS/VR Surveillance: Data to Action Results (continued): increased breastfeeding associations – breastfeeding in the hospital, receiving a support phone number, rooming-in, breastfeeding in the first hour post-delivery, feeding baby only breast milk Decreased breastfeeding associations – receiving a formula gift pack, hospital helping with breastfeeding, pacifier use Policy: outlines practices for developing “baby-friendly” hospitals

  31. Illinois PRAMS/VR Surveillance: Data to Action Evaluation: “Medicaid Family planning waiver, Illinois Healthy Woman program” (IL Dept of Healthcare and Family Services and University of Illinois – Chicago) Data: 2001-2007 PRAMS data Results: ongoing, being used to evaluate program effectiveness via PRAMS mothers’ experiences

  32. Illinois PRAMS/VR Surveillance: Data to Action Evaluation: “Women’s Mental Health policy and program development” Data: PRAMS data on post-partum depression Results: post-partum treatments offered new mothers varied considerably by race category, such as medication versus receiving counseling Policy: Illinois must take steps to ensure referral and treatment for all women and understand the factors influencing treatment selection.

  33. Example from Massachusetts PRAMS Their conclusions . . . • High proportion of MA women experienced postpartum depressive symptoms • Among those reporting depression, only 1 in 4 sought professional help • Improvements in screening for and appropriately managing PPD are needed

  34. Example from Massachusetts PRAMS :Policy Impact Used in drafting the bill “An Act Relative to Post Partum Depression” • Calls for task force to review PPD resources in MA and gather information on best practices and treatment options • Instructs MDPH to make PPD a priority • Mandates insurance companies to report to state on efforts to address PPD, including screening • Encourages providers, non-profits, health departments, and insurance companies to help de-stigmatize PPD and create a culture of awareness • Received favorable report from Joint Financial Services Committee; currently with the House Ways and Means Committee Supported the selection of a mental health priority for MCH Block Grant • “Promote emotional wellness and social connectedness across lifespan”

  35. Example from Massachusetts PRAMS : Programmatic Use MA New Parents Initiative • Program to improve health of new parents, infants and families across lifespan by enhancing communication between providers and new parents • Uses emotion-based messaging focusing on 4 topic areas • Improving maternal-infant mental health • Nurturing early care giving • Decreasing family violence • Increasing use of family planning among new parents • PRAMS PPD data contributed to the focus on improving maternal mental health

  36. Automated Influenza and PneumoniaReporting in anElectronicDeathRegistrationSystem validated with Laboratory ConfirmationAlvin T. Onaka, Ph.D. Hawaii Department of HealthOffice of Health Status Monitoring

  37. Influenza Deaths • CDC Case Definition Influenza listed anywhere on the death certificate (Part I or Part II) • Exclusions Haemophilus influenzae Parainfluenzae virus

  38. Weekly Mortality Reporting • Previous Procedure • Deaths due to Influenza or Pneumonia manually counted by DOH staff • No timely means of lab confirmation from other DOH branch that electronically reports lab data (ELR)

  39. Worksheet turned in to DOH and enters info. into system. DOH manually identifies Influenza & Pneumonia Weekly mortality report manually run from data system Funeral Director gives death worksheet to certifier to complete Previous Mortality Reporting Procedure

  40. Weekly Mortality Reporting • New Methodology • New automated method of identifying Influenza or Pneumonia cases via the Hawaii Electronic Death Registration System (EDRS) • New electronic laboratory confirmation between EDRS and ELR

  41. EDRS At scheduled times, mortality reports automatically sent via email to Disease Outbreak Control Division & Vital Statistics Internet Internet Certifier enters COD in EDRS NEDSS Later, Influenza/ Pneumonia lab results matched & results confirmed Current Weekly/Daily Mortality Reporting Procedure

  42. Automated Mortality Reports • Adjustable interval for either hourly, daily or weekly automated reporting • In addition to Pneumonia and Influenza case identification, custom reports are possible for future disease outbreaks (i.e., Syndromic Surveillance)

  43. User Defined Custom Reports

  44. Excel Spreadsheet Influenza & Pneumonia Indicators Full literal text of COD by certifier Unique decedent ID

  45. Automated Mortality Reports • Spreadsheet allows review of Influenza & Pneumonia status & pertinent certifier information for all deaths • With full COD available, trained reviewers can ignore computer triggered flags and request record review of possible Influenza cases missed by computer • Decedent privacy protected since DOH reviewers only see a Case ID

  46. Increasing Accuracy for Mortality Reporting and COD in general • Incorporation of spell checker from NCHS’s SuperMICAR • Spell checker reviews COD field and opens up a spell check box for certifier to accept or change possible misspelled word(s)

  47. Lessons Learned from H1N1 Surveillance • Medical Examiners and Coroners often certify record with “deferred – pending lab results”. • The current automated reporting system will miss these Influenza cases • Drop to paper cases include backdated certification information • Automated reporting system will also miss these cases

  48. Hawaii’s Confirmed H1N1 Influenza Death Cases • 13 confirmed cases occurred in about a 1 year time period. • About 24% cases first identified by the automatic email mortality reporting system • About 38% cases first identified by Vital Statistics staff who entered in COD that was completed non-electronically • About 38% cases first identified by Disease Outbreak Control Division (DOCD) staff from their Electronic Laboratory Reporting system (ELRS)

  49. In Summary . . . VRs as Public Health Surveillance • Monitoring emerging infectious diseases • Monitoring well-established infectious diseases • Monitoring chronic disease • Monitoring morbidity and mortality agents/factors • Monitoring health-related behaviors • Monitoring newborns’ outcomes/health • Monitoring quality of health care • Monitoring food, water, drugs, dairy, etc. • Nota bene: monitoring = collection, analysis and sharing of surveillance information

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