Juan Acuña M.D., MSc Professor of OB/GYN, Genetics, and Epidemiology Director Data, Information,...

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

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??

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

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

What drives us…

• Policy and political environment• Program planning, design, and

implementation• Evidence! • A strategy that unites them all

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

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

MCH-Related Data Sources & Systems

Cancer ARTHIVSTD

Vital Records

PNSSPedNSS BRFS

PRAMS Preg-Rel Mortality

Childhood Injury

YRBS

Birth DefectsNewborn Hearing

Child Lead

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

Data to Action= Opportunity into Results

Spin the Wheel...

KNOWLEDGE BASE

POLITICALWILL

SOCIALSTRATEGYFrom J Richmond

Community

Data Use “Triangle”Data & Analysis

Planning & Programs

Politics & Policy

TRANSLATION

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)

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

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

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

visiting programs

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

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)(+?)

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

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.

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

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:

Birth CertificatesPredictive Value Positive 76%Sensitivity 28%

Hospital Discharge DataPredictive Value Positive 85-95%Sensitivity 70-90%

Example # 3:

Evaluation of Data Studies

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”

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

Other issues:

Evidence-based processesCommunication-translation

Economic impacts

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?

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”

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.

Best Evidence

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

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.

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

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.

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

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

How do we make the change?

• (Donna will spend time talking about this in detail)

• About communication-translation, let’s do a short exercise:

Interaction in Public Health :

DATA PROGRAM

POLICY

questiongeneration area

email

Interaction in Public Health MCH

Adequate interaction:

DATA PROGRAM

POLICY

Good questiongeneration area

One more issue: resource allocation

Cost of “fixing” top 10 MCH Problems in your state

$

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