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![Page 1: Juan Acuña M.D., MSc Professor of OB/GYN, Genetics, and Epidemiology Director Data, Information, and Research Coordinating Center Florida International.](https://reader038.fdocuments.us/reader038/viewer/2022102722/5519d529550346695e8b4e0f/html5/thumbnails/1.jpg)
Insights into NA-Planning-Implementation-Change
Continuum in Public Health MCH
Juan Acuña M.D., MScProfessor of OB/GYN, Genetics, and Epidemiology
Director Data, Information, and Research Coordinating Center
Florida International University College of Medicine
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Other aspects of the 5 year “tune up”
Focus on time (deadlines, law, requirements, behavior change)
Maintenance processThings still go wrong!!How predictable are those :things”May be there is a greater picture!May be there are other issues not
fully covered??
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What is this all about….Process that influences several billion-dollar
expenditure in the US, in MCHOften decided in a “closed room”
environmentOften left exposed to very undesirable
methodological problems: bias and chanceAll about improving the health of women and
children, NOT about building pretty programsVery, very complex process addressing very
complex issues
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PH Services
Monitor health status YesDiagnose, investigate public hazards YesInform, educate, empower No/yesMobilize community partners NoDevelop policies and plans NoEnforce laws and regulations NoLink people to health services NoAssure expert workforce No
Evaluate effectiveness, access, quality YesResearch new insights and solutions Yes
Services stronglydata-driven
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What drives us…
• Policy and political environment• Program planning, design, and
implementation• Evidence! • A strategy that unites them all
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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
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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
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MCH-Related Data Sources & Systems
Cancer ARTHIVSTD
Vital Records
PNSSPedNSS BRFS
PRAMS Preg-Rel Mortality
Childhood Injury
YRBS
Birth DefectsNewborn Hearing
Child Lead
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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
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Data to Action= Opportunity into Results
Spin the Wheel...
KNOWLEDGE BASE
POLITICALWILL
SOCIALSTRATEGYFrom J Richmond
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Community
Data Use “Triangle”Data & Analysis
Planning & Programs
Politics & Policy
TRANSLATION
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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)
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Data supports that infant mortality might be impacted by nurse home visiting programs
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Data supports that infant mortality will be impacted by nurse home visiting programs
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Data supports that it is unlikely that infant mortality could be impacted by nurse home
visiting programs
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Data supports that infant mortality will not be impacted by nurse home visiting programs
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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)(+?)
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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
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Example #1:
Investment in Tobacco control, 2001 HighlightsU.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.
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National Conference
Community Systems-Building and Services Integration, 1997. HRSA
C. Earl Fox, M.D., M.P.H., ActingAdministrator, HRSA
“… community systems-building and services integration areStrategies need to be backed
by data that demonstrate not only what is being done but also what
works (evidence-based care)”
Example # 2
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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:
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Birth CertificatesPredictive Value Positive 76%Sensitivity 28%
Hospital Discharge DataPredictive Value Positive 85-95%Sensitivity 70-90%
Example # 3:
Evaluation of Data Studies
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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”
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Program-making and research1. Research occurs first and
programs are driven by it2. Programs occur first and
research is driven by them3. Programs and research are
created at the same time and feed one into each other
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Other issues:
Evidence-based processesCommunication-translation
Economic impacts
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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?
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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”
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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.
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Best Evidence
Available:• Published (strength of evidence)• Surveillance systems• Routinely collected information• Peer information• Smart opinion• Other
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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.
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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
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Evidence
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.
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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
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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
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How do we make the change?
• (Donna will spend time talking about this in detail)
• About communication-translation, let’s do a short exercise:
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Interaction in Public Health :
DATA PROGRAM
POLICY
questiongeneration area
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Interaction in Public Health MCH
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Adequate interaction:
DATA PROGRAM
POLICY
Good questiongeneration area
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One more issue: resource allocation
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Cost of “fixing” top 10 MCH Problems in your state
$
overkillwaistok