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Juan Acuña M.D., MSc Professor of OB/GYN, Genetics, and Epidemiology Director Data, Information, and Research Coordinating Center Florida International.

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Presentation on theme: "Juan Acuña M.D., MSc Professor of OB/GYN, Genetics, and Epidemiology Director Data, Information, and Research Coordinating Center Florida International."— Presentation transcript:

1 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 Monitor health statusYes Diagnose, investigate public hazardsYes Inform, educate, empowerNo/yes Mobilize community partnersNo Develop policies and plansNo Enforce laws and regulationsNo Link people to health servicesNo Assure expert workforceNo Evaluate effectiveness, access, qualityYes Research new insights and solutionsYes Servicesstrongly data-driven

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: Case recollection Population information Risk factor data (PRAMS) ANALYSIS Programs and policies RATES 1.absolute risk 2.population mapping 3.tendencies 1.Cause 2.risk factors 3.costs 4.morbidity Program evaluation

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

9 Example Perspectives in the health sector 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


11 Community Data Use Triangle Data & Analysis Planning & Programs Politics & Policy TRANSLATION

12 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)

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

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

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

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

17 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)(+?)

18 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

19 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.

20 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) Example # 2

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

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

23 exercise (30 minutes): 1. 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) 2. Work within your groups on the possible conceptual frameworks to assure that program and research (information gathering processes) truly connect

24 Program-making and research 1. Research occurs first and programs are driven by it 2. Programs occur first and research is driven by them 3. Programs and research are created at the same time and feed one into each other

25 Other issues: Evidence-based processes Communication-translation Economic impacts

26 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?

27 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

28 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.

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

30 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.

31 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

32 Evidence Statistical significance Meaningful to Public Health BOTH goodbestfair 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.

33 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

34 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 dont do D. Fair evidence to support dont do E. Good evidence to support dont do

35 How do we make the change? (Donna will spend time talking about this in detail) About communication-translation, lets do a short exercise:

36 DATA PROGRAM POLICY question generation area email

37 Interaction in Public Health MCH

38 DATA PROGRAM POLICY Good question generation area

39 One more issue: resource allocation

40 Cost of fixing top 10 MCH Problems in your state $ overkill waist ok

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