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Insights into NA-Planning-Implementation-Change Continuum in Public Health MCH

Insights into NA-Planning-Implementation-Change Continuum in Public Health MCH. Juan Acuña M.D., MSc Professor of OB/GYN, Genetics, and Epidemiology Director Data, Information, and Research Coordinating Center Florida International University College of Medicine.

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Insights into NA-Planning-Implementation-Change Continuum in Public Health MCH

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  1. Insights into NA-Planning-Implementation-Change Continuum in Public Health MCH Juan Acuña M.D., MSc Professor of OB/GYN, Genetics, and Epidemiology Director Data, Information, and Research Coordinating Center Florida International University College of Medicine

  2. Other aspects of the 5 year “tune up” • Focus on time (deadlines, law, requirements, behavior change) • Maintenance process • Things still go wrong!! • How predictable are those :things” • May be there is a greater picture! • May be there are other issues not fully covered??

  3. What is this all about…. • Process that influences several billion-dollar expenditure in the US, in MCH • Often decided in a “closed room” environment • Often left exposed to very undesirable methodological problems: bias and chance • All about improving the health of women and children, NOT about building pretty programs • Very, very complex process addressing very complex issues

  4. PH Services Services strongly data-driven Monitor health status Yes Diagnose, investigate public hazards Yes Inform, educate, empower No/yes Mobilize community partners No Develop policies and plans No Enforce laws and regulations No Link people to health services No Assure expert workforce No Evaluate effectiveness, access, quality Yes Research new insights and solutions Yes

  5. What drives us… • Policy and political environment • Program planning, design, and implementation • Evidence! • A strategy that unites them all

  6. Sources of evidence in PH • “soft” information: review processes, personal information, “gut” feelings • “adequate” information: routinely collected information, case review programs, passive systems • “strong” information: active surveillance, some clinical studies • “very strong”: randomized clinical trials

  7. Public Health data-action loop: Risk factor data (PRAMS) Case recollection ANALYSIS • Cause • risk factors • costs • morbidity RATES Population information • absolute risk • population “mapping” • tendencies Programs and policies Program evaluation

  8. Child Lead Newborn Hearing Birth Defects Childhood Injury PRAMS Preg-Rel Mortality YRBS Vital Records PedNSS PNSS BRFS ART STD HIV Cancer MCH-Related Data Sources & Systems

  9. CLINICAL Aims: Change the natural course of disease Technically feasible Ethically feasible Safe? Case-by-case Part of protocol PUBLIC HEALTH Aims: Lower prevalence Lower the incidence Lower the risk (factor) Primary prevention Program-based Population-based Example Perspectives in the health sector

  10. TRANSLATION Community Data Use“Triangle” Data & Analysis Planning & Programs Politics & Policy

  11. Exercise: For the following statements please: …grade them from 0 to 10, based on what you read, not on what you know being: • 0: the causal relationship is not possible or will not happen • 10: the association suggested will happen for sure (no chance that it will not happen)

  12. Data supports that infant mortality might be impacted by nurse home visiting programs

  13. Data supports that infant mortality will be impacted by nurse home visiting programs

  14. Data supports that it is unlikely that infant mortality could be impacted by nurse home visiting programs

  15. Data supports that infant mortality will not be impacted by nurse home visiting programs

  16. LBW - SGA LAPRAMS data 1998-1999 Population at risk LA 1998-1999: 130,294 pregnancies Smoking OR: 3.5 Wt-Gain OR: 3 Counseling OR: 1.7 Prevalence: LBW: 7% (9,120) VLBW: 2% (2,605) SGA: 15% (19,544) AFp: LBW: 9% (820)(+?) VLBW: 2% (52) (+?) SGA: 2% (390)(+?)

  17. Why the concern? • Knowledge is rapidly expanding • The use of “EB decision-making” is common • Large amount of published (scientific) literature • Larger amounts of (unused) stored data • Lack of guidelines for the EB process • Large degree of uncertainty about change

  18. Example #1: Investment in Tobacco control, 2001 Highlights U.S. Department of Health and Human Services Centers for Disease Control and Prevention “Our lack of greater progress in tobacco control is more the result of our failure to implement proven strategies than it is the lack of knowledge about what to do.” “…this is cause for concern because the costs associated with smoking-related diseases will continue to grow unless evidence-based programs are implemented” David Satcher M.D.

  19. Example # 2 National Conference Community Systems-Building and Services Integration, 1997. HRSA C. Earl Fox, M.D., M.P.H., Acting Administrator, HRSA “… community systems-building and services integration are Strategies need to be backed by data that demonstrate not only what is being done but also what works (evidence-based care)”

  20. Example # 3: • Prevalence rates • Registry of cases for study or referral • Monitor prevention Surveillance Systems • Risk factors • Protective factors • Public concerns Epidemiological Studies • Prevention strategies • Public policy • Education Prevention Programs

  21. Example # 3:Evaluation of Data Studies Birth Certificates Predictive Value Positive 76% Sensitivity 28% Hospital Discharge Data Predictive Value Positive 85-95% Sensitivity 70-90%

  22. exercise (30 minutes): • Now that you have “performed” your needs assessment, please identify what other issues could preclude you from making (or being able to make) the desired change(s) • Work within your groups on the possible conceptual frameworks to assure that program and research (information gathering processes) truly “connect”

  23. Program-making and research • Research occurs first and programs are driven by it • Programs occur first and research is driven by them • Programs and research are created at the same time and feed one into each other

  24. Other issues: • Evidence-based processes • Communication-translation • Economic impacts

  25. Conflict in PH To do things right To do the right things DRIVING FORCE: best evidence for the best practice PROBLEMS: How is this done? How to do it always? How to do it always the same?

  26. A more “modern” conflict: Making the right choice • Health Economics, Clinical Economics, Prevention Efectiveness • Cost-Benefit (cost vs. monetary outcome) • Cost-Effectiveness (cost vs. natural outcome) • Cost-Utility (cost vs. standardized adjusted outcome) Bottom line: which alternative gives the best “bang for the buck”

  27. Some efforts • The Agency for Healthcare Research and Quality (AHRQ) was established in 1989 • established it as the lead Federal agency for enhancing the quality, appropriateness, and effectiveness of health services and access to such services.

  28. Best Evidence Available: • Published (strength of evidence) • Surveillance systems • Routinely collected information • Peer information • Smart opinion • Other

  29. Other sources of best evidence • Meta-analyses, cost effectiveness analyses, decision analyses • Update PH reports and assessments • Undertake quantitative/qualitative research when possible • Evidence-based teaching and training opportunities • Provide technical assistance to organizations that seek EBPH • Dissemination strategies for EBPH products • Scan published and lay literature to identify ripe topics • Evaluation of programs and projects on the quality of interventions and its relevance on outcomes and prevention effectiveness of health care.

  30. Evidence I - Evidence from RCT II-1 - Well designed non-randomized trials II-2 - Cohort, Case Control analysis II-3 - Comparisons of places, time, interventions, better more than 1 center III - Opinion of authorities, descriptive studies, expert peer groups or committees

  31. Statistical significance Meaningful to Public Health BOTH good best fair We have been taught to accept statistical significance. If large samples (as in many cases), we are bound to have it, even if it is not meaningful. Evidence

  32. Change PH practices Public Health is about: • Research • Advocacy • Community Services • Education • Wisely invest as little money as possible to make the biggest and better change possible

  33. Changes are based on recommendations A. Good evidence to support decisions B. Fair evidence to support decisions C. Poor evidence that does not provide direction to do or don’t do D. Fair evidence to support don’t do E. Good evidence to support don’t do

  34. How do we make the change? • (Donna will spend time talking about this in detail) • About communication-translation, let’s do a short exercise:

  35. Interaction in Public Health : PROGRAM DATA email question generation area POLICY

  36. Interaction in Public Health MCH

  37. Adequate interaction: DATA PROGRAM Good question generation area POLICY

  38. One more issue: resource allocation

  39. Cost of “fixing” top 10 MCH Problems in your state waist overkill ok $

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