introduction & overview: MAKING DATA WORK FOR YOU

1. Laurin Kasehagen, MA, PhD MCH Epidemiologist / CDC Assignee to CityMatCH Maternal & Child Health Epidemiology Program Applied Sciences Branch, Division of Reproductive Health National Center for Chronic Disease Prevention & Health Promotion introduction & overview:MAKING DATA WORK FOR YOU

2. Workshop Objectives • Locate and obtain data on child health and school readiness from national surveillance systems • Explain some limitations of local level data and how these limitations can affect conclusions • Describe ways in which outcomes for cities and counties differ from each other and from state / national outcomes • Provide hands-on opportunity to work with surveillance data

3. Introduction & Overview • Importance of local data • Introduction to local data sources • What’s available • How to use it • Advantages and limitations of these data sources • How city/county level outcomes differ from state/national level outcomes

4. Why is local data important? • “. . . . [Such] data are • essential in identifying communities most at risk of poor health outcomes, • exploring the determinants of such variations in health, and • ultimately guiding community health programs and policies.” • From: Shah, Whitman & Silva in “Variations in the Health Conditions of 6 Chicago Community Areas: A Case for Local-Level Data”

5. Misperceptions about local-level data • Not available • Not accessible • Too difficult to use • Don’t have what I need

9. WOW! There are a lot of different data sources! Where do I start? • Known Data Source • Topic • Initiative / Indicator • Existing indicators linked to data sources • Geography

12. Ways to measure the health of children under 10 years of age • Healthy People 2020 • 42 Topic Areas • 1,412 Objectives • 262 are developmental objectives • Over 160 HP2020 objectives relate to children • Maternal, Infant, and Child Health • Early and Middle Childhood • Immunization and Infectious Diseases • Nutrition and Weight Status • Oral Health • Physical Activity • Local-level data is not available for all of these objectives

13. Finding Local-Level Data Examples • HealthyPeople.gov • http://healthypeople.gov/2020/default.aspx • Mental Health Status Improvement • HP2020 Objective MHMD-6: Treatment for children with mental health problems • Maternal, Infant, and Child Health • HP2020 Objective MICH-30: Increase the proportion of child, including those with special health care needs, who have access to a medical home • Early and Middle Childhood • HP2020 Objective EMC-2.3: Increase the proportion of parents who read to their young child

14. HP2020 Objective MHMD-6: Treatment for children with mental health problems; Data compiled at the Health Indicators Warehouse from the 2008 National Health Interview Survey 68.90%(62.70%, 75.10%) 82.51% (74.84%, 90.19%) 57.97% (48.79%, 67.15%)

15. Is there local-level information on reading to young children? Website: http://www.nschdata.org/Content/Default.aspx; accessed 07/26/2011.

16. Data on reading to young children is available on-line at the national, regional, state levels

17. On-line data can be looked at and compared through a number of different subgroups

18. How can you tell whether the statistic that you have is significant?

19. How can you tell whether the statistic that you have is significant?

20. Community Health Status IndicatorsComparison of Peer Counties: Risk Factors for Premature Death Boone County, MO Deschutes County, OR Santa Fe County, NM

21. Why is local-level data so hard to get? • Data may not be collected at the household, city, or county level • e.g., some national and state surveys collect data only for large metropolitan areas, or select a few areas to represent all • Data may be reported only for jurisdictions with populations of 100,000 or more • For smaller jurisdictions, data may be aggregated

22. If you do get your hands on local data, what do you need to watch out for? • Confidentiality • e.g., “Hey look, this dataset has a family of 12 kids, including 3 sets of twins! I’ll bet that’s the Smiths from down the street. Whoa, it says here that Mrs. Smith only has an 8th grade education.” • Rare events / small numbers of “events” (death, birth defects) • limited statistical power • several years may need to be combined

23. National, state, or MMSA level data may not capture events at the local level

24. Small populations / rare events in a population can produce dramatic, erratic results

25. Other concerns about data . . . • Where did the numbers came from? • a source that collects information on ALL of the population (e.g. Census, Vital Records) • a representative sample of the population • a convenience sample of some people • Quality of data is affected by practices related to • Recording • Collecting • Reporting • Potential sources of data differ greatly per type of event – deaths, injuries, illnesses require complex surveillance systems

26. Limitations: How well do county data represent city populations? • Some public health issues that affect cities will not be accurately represented using county-level data • Generally, counties are larger geographical areas that also include suburban and even rural areas • Some large cities are spread over several counties • Some counties do not include their urban central city

27. Evidence-Based Practice for Public Healthhttp://library.umassmed.edu/ebpph/

28. The Community Guide (CDC)http://www.thecommunityguide.org/

29. Health Policy Guidehttp://healthpolicyguide.org

30. PHPartnershttp://phpartners.org/

31. YOU can put the pieces together • National vital records and state survey data provide broad context • “Where do we stand?” • Local survey and subpopulation data allow us to dig deeper • “What are underlying causes?” • Local investigation and action • “What do we know about our systems, our communities?”

32. http://www.citymatch.org/citylights.php

33. CityMatCH Mission Improving the health and well-being of urban women, children and families by strengthening public health organizations and leaders in their communities

34. References • Shah, Whitman & Silva. Variations in the Health Conditions of 6 Chicago Community Areas: A Case for Local-Level Data. Am J Public Health 96(8): 1485-91 (2006). • Brownson, et al. Evidence-Based Decision-Making in Public Health. J Public Health Manag Prac 5:86-87 (1999). • The Cochrane Collaboration; www.cochrane.org • Association of Maternal and Child Health Programs (AMCHP) Best Practices; http://www.amchp.org/AboutAMCHP/BestPractices/Pages/default.aspx • National Association of City and County Health Officials; http://www.naccho.org/

35. Contact Information Laurin Kasehagen, PhD, MA Senior MCH Epidemiologist / CDC Assignee to CityMatCH lkasehagen@unmc.edu lkasehagen@cdc.gov CityMatCH at the University of Nebraska Medical Center Department of Pediatrics 982170 Nebraska Medical Center Omaha, NE 68198-2170 402-561-7500