utilizing local data to catalyze coalitions, address ... · utilizing local data to catalyze...

Utilizing Local Data to Catalyze Coalitions, Address Disparities,

and Improve HealthMelissa Jordan, MS

Florida Department of Health

Eva Wong, PhDPublic Health Seatt le-King County

Vickie Boothe, MPHCenters for Disease Control and Prevention

Association for Community Health Improvement ConferenceMarch , 2016

Department of Health and Human ServicesCenters for Disease Control and Prevention

Presenter

Presentation Notes

Add Amy’s info

Learning Object ives

Utilize public health resources to meet CHNA requirements:

Local measures of population health & determinants Identify “hot spots” of health disparities; Understand underlying social determinants driving disparities Catalyze collective actions Raise awareness on the role of place-based factors in creating health

Select & evaluate interventions Strongest evidence Higher impact Cost effective

Final 2014 IRS CHNA Regulat ions The hospital must “solicit” and “take into account” input from a state or local

health department and members of medically underserved (disparately impacted), low-income, and minority populations.

The hospital may use any criteria to prioritize the significant health needs it identifies including: burden, scope, severity, or urgency of the health need; the estimated feasibility and effectiveness of possible interventions; the health disparities associated with the need; or importance the community places on addressing the need.

Health needs may include “financial and other barriers to accessing care, preventing illness, ensuring adequate nutrition, or social, behavior and environmental factors that influence health in the community.”

2016 CHNAs must include an impact evaluation of the actions in the previous CHNA.

Presenter

Presentation Notes

CHNAs initiated after 12/29/2015 must include an impact evaluation

State Public Health Resourcesfor

Community Health Needs Assessments

N E I G H B O R H O O D - L E V E L M E A S U R E S O F P O P U L A T I O N H E A L T H A N D S O C I A L D E T E R M I N A N T S I N F L O R I D A

M E L I S S A M . J O R D A N , M S

C O L L A B O R A T O R S : K R I S T I N A K I N T Z I G E R , P H D

C H R I S D U C L O S , M S , G I S P

A S S O C I A T I O N F O R C O M M U N I T Y H E A L T H I M P R O V E M E N T2 0 1 6 N A T I O N A L C O N F E R E N C E

M A R C H 2 , 2 0 1 6

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Sustainable Population Health Improvement: Tools for the Journey

Life Expectancy Project

CDC/Council of State and Territorial Epidemiologists (CSTE) Workgroup

Goal: Develop, pilot, and disseminate a stakeholder driven, easy-to-use Guide for Calculating Life Expectancy Estimates at the Sub-County Level that allows states and local health departments to create small-area life expectancy to examine health disparities and compare across localities.

Sub-County Assessment of Life Expectancy (SCALE) Phase 1

Timeline: October 2014 –June 2015

Phase I Participants CDC: Population Health Metrics Team State/local: Florida, Los Angeles County, Maine, Massachusetts,

New York State, Seattle & King County, Washington, Wisconsin

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Life Expectancy – Part II

SCALE Phase II

Goals Recruit states/locals to test the tool and Guide Expand coverage for areas without sufficient populations Engage subject matter experts on best practices for life

expectancy visualization (mapping) and messaging

Timeline: August 2015 –June 2016

Approximately 25 state and local health departments participating

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Why Life Expectancy?8

Life expectancy at birth Number of years a newborn can expect to live if current age

specific mortality rates in that population remained the same over time

Allows us to look at disparities by place Identify areas where underlying health behaviors,

social determinants, and other community factors may be targeted for public health intervention

Presenter

Presentation Notes

Life expectancy at birth is defined as the estimated number of years a newborn can expect to live if current age specific mortality rates in that population remained the same over time. [7] Life expectancy at birth is a common measure used to routinely monitor health inequalities across countries. [8, 9]. Given that life expectancy and survivorship is increasing in many places, there is some concern that the gap may be widening between those who experience greater longevity versus those who do not. (Murray, C et al 2006) REFLECTS THE IMPACT OF MAJOR ILLNESSES AND INJURIES AND THEIR UNDERLYING CAUSES, INCLUDING THE SOCIAL, ECONOMIC, AND ENVIRONMENTAL CONDITIONS WHICH INFLUENCE HEALTH.

Example: Leon CountyZIP Codes = 10, Population = 276,000

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Gender County-Level Estimate (years)

Both 79.39

Males 76.97

Females 81.62

Gender Min/Max

ZIP Code

ZIP Code-Level Estimate (years)

Both Min 32310 74.16

Max 32312 81.72

Males Min 32310 70.42

Max 32312 80.36

Females Min 32310 78.13

Max 32309 84.18

Rural South FloridaCounties = 5, ZIP Codes = 19, Population = 214,600

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Gender DeSoto Glades Hardee Highlands Okeechobee

Both 80.53 80.44 79.55 79.42 77.10

Males 79.14 78.25 77.64 76.79 75.35

Females 82.29 83.56 81.68 82.17 79.18

Gender Min/Max County ZIP

CodeZIP Code-Level

Estimate (years)

Both Min Okeechobee 34972 75.73

Max DeSoto 34269 84.12

Males Min Glades 34974 74.36


Females Min Okeechobee 34972 77.02


ZIP Code-Level Life Expectancy Estimates,

Both Genders, Rural Counties, FL, 2009-2013

Life Expectancy: Limitations14

Suppression based on low numbers of deaths and/or population Geographic aggregation Mapping special facilities or populations (e.g., universities,

prisons, assisted living facilities)

ZIP Code to ZCTA (ZIP Code Tabulation Area) linkage

Changing ZIP Code boundaries

Incorporating Social Determinants of Health (SDoH)

SDoH variables are factors in the social and/or built environment that may improve or diminish the health of individuals and communities.

These factors have repeatedly been found to be associated with heart disease and stroke in the U.S.

Such area-level factors have been used to match prevention programs and policies to the needs of communities.

Example: SDoH & Acute MI Morbidity

To characterize the relationship between neighborhood-level characteristics and rate of acute MI Florida residents Years 2008-2012 ZIP Code level Using emergency department (ED) and inpatient hospitalizations

Examine variables individually and combined for the outcomes of interest

Understand both the global and local relationship of these variables with chronic disease morbidity/ mortality

• Male (%)• Race/ethnicity (%): African American,

Hispanic, minority• Age 65 years and older (%)• Married (%)

Demographic

• Median household income ($)• Below poverty level (%)• Unemployed (%)• Education (%): less than high school, high

school, college• Rural (%)

Socioeconomic

• Median household value ($)• Owner-occupied housing units (%)

Housing/Built Environment

• Number of MI-related ED and hospital visits• Crude rate per 100,000• Age-adjusted rate per 100,000

Acute Myocardial Infarction

Soci

al D

eter

min

ants

of H

ealt

h

Rural areas in central and north Florida and the

Panhandle

Where is the greatest impact of acute MI in FL?

Presenter

Presentation Notes

Higher age-adjusted rates of acute myocardial infarction (MI) emergency department (ED) visits and hospitalizations occurred in areas in central Florida, north central Florida, and the panhandle. Hotspot clustering refers to any area within the study region of significant elevated risk. Hotspots are determined using the Gi statistic, and then Z scores are calculated to determine levels of significance. The results from this analysis indicate that clusters of high rates exist in Florida, and few clusters of low rates were identified.

Bivariate OLS Associations Between SDoH Variables and MI RatesVariable Coefficient Std Error p-value Robust p-value% Male 46.5899 6.1996 <0.0001 0.0205% African American 7.4856 1.7879 <0.0001 0.0017% Hispanic 1.4084 1.5336 0.3587 0.3710% Minority 8.0421 1.6418 <0.0001 0.0160% Aged 65 Years and Older -7.5539 2.6205 0.0040 0.0056% Married -14.3978 2.3866 <0.0001 0.0010Median Household Income ($) -0.0172 0.0013 <0.0001 <0.0001% Below Poverty Level 32.7632 2.9563 <0.0001 <0.0001% Unemployed 14.1161 10.9829 0.1990 0.5201% Less than High School Diploma 44.4410 2.6415 <0.0001 0.0002% High School Diploma -44.4410 2.6415 <0.0001 0.0002% College Degree -27.0983 1.9233 <0.0001 <0.0001% Rural 6.3550 0.8169 <0.0001 <0.0001Median Household Value ($) -0.0027 0.0002 <0.0001 <0.0001% Owner-Occupied Housing -2.4367 1.9230 0.2054 0.1546

Local Statistics

Presenter

Presentation Notes

The map on the left shows the local coefficients for the proportion of the population that is considered rural, adjusted for the proportion of the population with a high school diploma. The darker areas represent ZCTAs with a stronger direct associations, whereas the lighter areas represent the areas with stronger inverse associations. The map on the right shows the local coefficients for the proportion of the population with a high school diploma or higher, adjusted for the proportion of the rural population in the ZCTA. Lighter areas represent stronger inverse associations, and darker areas are actually weaker inverse associations.

Environmental Public Health Tracking Network

Example: Sub-County Data Tools

Customized Community Profiles – by Topic

End User Aggregation

Future Work

Incorporate life expectancy and SDoH measures into existing community profile reports and data visualization tools Collaboration with other public health programs (e.g., chronic disease,

lead) and local health departments Combine data on environmental hazards, health outcomes, and social

vulnerability

Support more local activities Community health assessments Community health improvement plans Health impact assessments

Continue discussions on limitations and concerns with sub-county data and solutions

www.FloridaHealth.gov

Melissa JordanSenior Environmental Epidemiologist

(850) [email protected]

To protect, promote, & improve the health of all people in Florida through integrated state, county, & community efforts.

Florida Department of HealthDivision of Community Health Promotion

Public Health Research Unit

Local Public Health Resourcesfor

Community Health Needs Assessments

Eva Wong, PhDPublic Health-Seattle & King County

[email protected]

Impact on Population Health

Presenter

Presentation Notes

How the Pub Health Systems interface with medical care to address issues of equity.

Institute for Health Metrics and Evaluation

Life expectancy, by county, compared to the world’s 10 best countries

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As just one example, overall, at a county level King County appears to do well, in comparison to many other areas in the U.S. We rank highly in quality of life, social factors, overall income, general health, and low in risk behaviors.

Life Expectancy in King County by Census Tract

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• Difference of 30 years! (Low of 66; High of 96)

• King County Average: 81.6

• Tracts with the lowest life expectancy are more than 40 years behind the longest lived countries

Presenter

Presentation Notes

But the county level data masks very large and significant disparities. We mapped our small area life expectancy at birth (by census tract), and it showed a high degree of variability by sub-county area, rivaling the disparities seen across the nation.

Lack of Physical Activity Diabetes

Life Expectancy Tobacco Use Frequent Mental Distress Adverse Childhood Experiences

Preventable HospitalizationObesity

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Presenter

Presentation Notes

This is not confined to life expectancy. Other area estimation around mental health, risk factors, and other quality of life indicators shows similar geographic patterns.

Communities of Opportunity

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Health, social and economic measures by census tractMeasures Lowest decile Highest decile

Life expectancy 74 years 87 years

Health, broadly defined

Adverse childhood experiences

20% 9%

Frequent mental distress

14% 4%

Smoking 20% 5%

Obesity 33% 14%

Diabetes 13% 5%

Preventable hospitalizations

1.0% 0.4%

Housing

Poor housing condition

8% 0%

Economic opportunity

Low-income, below 200% poverty

54% 6%

Unemployment 13% 3%

Presenter

Presentation Notes

Comparison of the top and bottom deciles demonstrate the disparity. Led to the COO project, a joint collaboration between The Seattle Foundation, Living Cities, Robert Wood Johnson, and the WA State Accountable Communities of Health grant�Key to the success are cross-departmental collaborations: evaluation, planning and other workgroups Moved funds into the community – for policy and systems change in 3 cities (SeaTac/Tukwila, Rainier Valley (S. Seattle), and White Center/N. highline) and 2 learning communities (Auburn and Skyway). 12 policy and systems change grants were awarded, with a second round in March of 2016. Aligned with other county-wide initiatives. (don’t want to derail the convo, but could also segue into BSK, which is also supporting this work)

Shared Measurement Supported Prioritization of Goals and Communities

• Policy and system change

• Place-based investment in neighborhoods

• Toolkits and Learning Community to support all of King County

Community voice

Continuous communicati

on

Backbone function

Mutually reinforcing activities

Shared measurement

system

Common agenda

Collective Impact

Presenter

Presentation Notes

Collective impact models do not always include the community voice, but it was an essential function to include. Large-scale social change requires broad cross-sector coordination, yet the social sector is frequently focused on the isolated intervention of individual organizations. Collective impact is a long-term commitment across sectors to set on a common agend for solving specific social problem(s). It uses a shared measurement system (do the definitions mean the same thing?) and are staffed by an independent backbone organization. There are 3 main ways COO is investing in communities. (see slide)��Helping the communities develop the common agenda and shared measurement system is occurring through the Results Based Accountability Framework, keeping in mind the community voice.

Using Results Based Accountability

• Shared result: All children, adults, and communities in King County are healthy, have quality/affordable homes, thrive economically, and feel like they are part of a community

Health

• Consumption healthy foods

• Active living• Use of preventive

care (physical/mental/ dental)

Housing

• Households paying < 50% income on housing

• Housing stability (prevent involuntary displacement

Economic Opportunity

• Unemployment• Income above

200% of poverty• Business stability

(prevent involuntary displacement)

Connection to Community

• Youth who have adult to turn to for help

• Adults who have emotional support

A Process Framework

RBA is a framework that offers a disciplined way of thinking and taking action that:• Communities can use to improve the conditions of well being for children, youth, families and the whole community, and • Leaders can use to improve the quality and effectiveness of programs, policies, agencies and service systems.

Why RBA?

Presenter

Presentation Notes

Achieving results is the way in which we make a difference. In order to do so, we need a collective effort that is organized, disciplined, focused and committed to a data-driven decision-making process. We must change mindsets about using data simply to comply or as a punitive method. We must also reconsider the notion that our programs, services, agencies and providers actually may not be doing the best they can do or providing the best services they are providing.

Economic Opportunity: Baseline

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Presenter

Presentation Notes

Select indicators with data power (quality, timely, credible.) Does it have proxy power? Communication powerHow are we doing? Looking at the baseline, forecast, and trend. Story behind the curve is that King County has higher employment rate and much higher income. The change in WC may be due to changes in population (gentrification)

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What’s the Story Behind the Curve?

Presenter

Presentation Notes

Slide 3: “Moderate” income is income > 200% FPL: need better podium power description of this indicator “secure housing” is among renters

Communities Contributing Data

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• Sites are supplementing the COO baseline data report with community data– Community survey/program data– Qualitative data

• Data for indicators that cut across the sites:– Housing affordability and quality– Employment– Wages– Business ownership and stability– Education

Presenter

Presentation Notes

* Adds context that quantitative data might miss; is the story behind the data.

Next Questions

• Partners come to the table• Develop the shared measurement system• Determine what works to turn the curve• Propose actions• Evaluate performance measures:

– What did we do?– How well did we do it?– Is anyone better off?

Presenter

Presentation Notes

Bring together partners who are working on any aspect of the story behind the curve. * In developing a shared measurement system Common performance measures reported Use a system that all partners can access * Vickie will talk more about the evidence base on what works to turn the curve.�* Turn the Curve action planning with partners has two very important benefits: Gives the opportunity for partner commitment and contribution Reduces unnecessary overlap or duplication This may be especially important to hospitals who have moved towards the implementation phase of their Community Health Needs Assessment – being able to know the other actors, leverage their resources, and get the most bang for the investment buck in the community. Strengthening community ties benefits organizations as well as the community.

How Do You Measure Change?

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Policy change•Written laws, regulations, programs, procedures or budget

commitments, as adopted by a formal governing body• Smoke-free multi-unit housing , parks, and other settings• Swimming pool scholarship program

Infrastructure change

• Sustainable changes in practices & protocols withininstitutions

•Routinely training cafeteria staff on whole food cooking• Increase f/v purchasing by 20% per year

System change •New, ongoing interactions across sectors

• Farm to schools/childcare/senior centers•City planning and public health departments

Environment and social

change

•Physical, economic and social surroundings•Hydration stations•Placement of items in school cafeterias•Promoting foods using superhero themes

Behavior change

•Current smoker, 2nd hand smoke•Obesity, overweight•Physical activity•Healthy eating

REACH

Presenter

Presentation Notes

Amy: I am lukewarm about this – I’ve used it before but think sometimes it’s helpful for people not super familiar with the concept of how to define the changes or in thinking through even a basic logic model. This could speak to the general evaluation section. Policy change Changes in written laws, regulations, programs, procedures or budget commitments, as adopted by a formal governing body designed to influence personal and organizational behavior in both public and private sectors. Smoke-free multi-unit housing , parks, or other settings Swimming pool scholarship program Infrastructure change Sustainable changes in practices and protocols within institutions and organizations designed to influence behaviors that do not involve formal written policies. Systems change Changes in the way organizations in different sectors interact with each other in an ongoing way (therefore changes within a single organization, e.g., one city agency or one school district, constitutes a policy or infrastructure change, not a systems change). May or may not be supported by formal/written policy changes. Environmental change Changes in the physical, economic and social surroundings that have the potential to influence the behavior of a significant number of people (i.e., is not limited to an individual family/home environment).

Beware of Data Issues for Small Areas

• BRFSS data concerns– Standard reporting guidelines would result in data

suppression for some indicators at certain sites– 5-year rolling averages limits ability to make

conclusions about change over time– Which indicators of use of preventive care are most

relevant?– Missing information on some residents (given in

English and Spanish only)• Need relationships with school districts for

permission to release school district-level data

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Presenter

Presentation Notes

Was thinking about addressing data limitations when doing some of this work. �BRFSS data: Turn to community survey data and other data sources to help address limitations from BRFSS data. Considering options of oversample for smaller resolution data. Need to balance need for local estimates with unintended consequences from unstable estimates for internal and external audiences.

Federal Public Health Resourcesfor

Community Health Needs AssessmentsOverview

Vickie Boothe, MPHActing Senior Evaluator

Division of Community HealthCenters for Disease Control and Prevention

Sub-County Assessment of Life Expectancy (SCALE) Project

Three Year Project CDC, Council for State and Territorial Epidemiologists, 8 Health Departments

Goal Develop and disseminate a stakeholder driven, easy to use Guide for

Calculating and Visualizing Life Expectancy Estimates at the Census Tract Level

Status: Draft Guide & Software Pilots 25 State & Local Health Departments Completion June ‘16

Pract ice & Research Applicat ions Identify and monitor community hot spots of health disparities Investigate associated behavioral, social and environmental factors Catalyze multi-sector actions on place based SDOH

Presenter

Presentation Notes

We are on tract to have a draft guide and software available by June 2015 and we will be looking for health department volunteers to pilot the guide.

Area-based Socioeconomic MeasuresHealth Disparities by Census Tract Poverty

Harvard Geocoding Projects I & II

MA and RI 1990 Data 5 Leading Causes of Death and

Cancer Sites, HIV, Homicide

Different area sizes and SES measures

Census Tract (CT) Poverty Detected Disparit ies Missed by other measures

> Disparities by race/ethnicity

Within race/ethnic groups

Source: Harvard Geocoding Projects I & II (Nancy Krieger)1998 – 2005

Presenter

Presentation Notes

Other measures (e.g., education, wealth, unemployment, SES indices) and geographic units (zip codes)

http://www.hsph.harvard.edu/thegeocodingproject/

CSTE Disparit ies Pilot Project

11 States and localities

Multiple health outcomes

CT > 20% in poverty

Consistently associated with greatest disparities

Reflect confluence of neighborhood risk factors including unemployment, deteriorated housing, violent crime, material resource access, behavioral factors, access to care, and pollution.*

*Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: The Public Health Disparities Geocoding Project. Am J Epidemiol 2002; 156:471-482.

CSTE Pilot : Harvard Geocoding Project

http://www.cste.org/?CSTEPilotProject

Age-adjusted Mortality Rate by % in census tract who live

below poverty, NYC, 2000

0

2

4

6

8

10

12

<5% 5-9% 10-19% 20-29% 30-39% 40+%

Dea

th R

ate

per 1

000

Percent below poverty in census tractSource: Hadler J. Analysis of Public Health Data Using Census Tract-level Poverty

CSTE Pilot : New York City Example

Presenter

Presentation Notes

CSTE Pilot Project Results NYC

http://www.cste.org/resource/resmgr/.../CSTEWebinarCTPovertyAnalysis

Age-adjusted Mortality Rate by % in census tract who live

below poverty by race/ethnicity, NYC, 2000

02468

101214

White (non-H) Black (non-H) Hispanic Asian

<5% 5-9% 10-19% 20-29% 30-39% 40+%

Dea

th R

ate

per 1

000

Percent below poverty in census tract

CSTE Pilot : New York City Example

Presenter

Presentation Notes

Based on the demographic distribution, approximately half of the disparities in premature death would be missed with the traditional focus on race and ethnicity.

Balt imore Poverty vs Life Expectancy

Joint Center for Political and Economic Studies. Place Matters for Health in Baltimore: Ensuring Opportunities for Good Health for All. Sept. 2012

Presenter

Presentation Notes

Note: CHSI, Community Commons and other census tract maps can be useful for identifying disparities in social factors such as poverty and educational attainment. However, as this map demonstrates, there is not a 1:1 relationship between those social factors and health outcome disparities such as LE.

NEW SCIENTIFIC RESOURCES FOR SELECTING AND EVALUATING INTERVENTIONS

Presenter

Presentation Notes

Our first product relates to the very first questions we received, which was, with all of the various indicator sets and available data, where do you even get started?

3 Buckets of Prevent ion

Source: Auerbach, J. (2015). "The 3 Buckets of Prevention." J Public Health Manag Pract.

Bucket 3 List : Methods

Highest evidence level rat ing: University of Wisconsin County Health

Rankings & Roadmaps What Works for Health

Secondary source (QA/QC) The Guide to Community Preventive Services

Excluded all clinical interventions Bucket 1: Traditional Clinical Bucket 2: Innovative Clinical

• 6/18 Initiative

Results: 150 Potent ial Intervent ions One or more systematic reviews

Presenter

Presentation Notes

CHR&R Sources:: 5 Systematic Review Websites (Cochrane, Community Guide, Campbell Collection; 10 Rating Organizations (AHRQ, SAMSHA); 7 Sites of Published and Grey Literature (RAND, CDC, C

http://www.countyhealthrankings.org/policies/

http://www.thecommunityguide.org/index.html

http://www.cdc.gov/sixeighteen/

Bucket 3 List : Methods Intervent ion Inclusion Criteria 5 Year Timeframe

• Measurable outcomes, or • Surrogate measures

o Causally linked to outcomeso Readily available

Costs data• Cost savings or neutral

Lack of saturation • Implemented < 50% of States

CDC Program Review Additional interventions considered

Presenter

Presentation Notes

Outcomes directly measurable within 5 years include infant mortality, preventable ED visits and hospitalization Surrogate measures include minimum recommended levels of physical activity, smoking cessation rates

Bucket 3 List : Results 26 Intervent ions met all criteria

Total population Subpopulations

• behavioral risk factors (e.g., smoking)• low income

Social determinants of health

Organized by life stage Before birth & infancy Early childhood School Age Young Adults Adults Older Adults All Life Stages/SDOH

Bucket 3 List : Results Before Birth & Infancy

Breastfeeding (Baby Friendly Hospitals) Pregnancy Peer Support Programs

(Centering Pregnancy) Early Childhood Home Visitation Programs

Early Childhood Head Start Center-Based Early Childhood Education Pre-kindergarten

Early Adult & Adult Worksite obesity prevention Universal Motorcycle Helmet Laws Alcohol Unit Price for Increase Tobacco Unit Price Increase Comprehensive statewide tobacco program Mass-Reach Health Communication

Interventions for Tobacco Syringe Exchange Program

School Age Multi-component obesity prevention Safe Routes to School Family Group Program (Family and

Schools Together) Multidimensional Foster Care Treatment School Based Violence Prevention

Programs

Older Adult Activity Programs for Older Adults (includes

Multi-component fall prevention)

http://www.countyhealthrankings.org/policies/breastfeeding-promotion-programs

http://www.countyhealthrankings.org/policies/early-head-start-ehs

http://www.countyhealthrankings.org/policies/universal-pre-kindergarten

http://www.countyhealthrankings.org/policies/worksite-obesity-prevention-interventions

http://www.countyhealthrankings.org/policies/families-and-schools-together-fast

http://www.thecommunityguide.org/violence/schoolbasedprograms.html

Bucket 3 List : Results Social Determinants of Health

Public Transportation: System Introduction or Expansion Clean Diesel Technology Fleet Transition Smoke-free policies: Indoor areas Housing Choice Voucher Program House Rehabilitation Loan and Grant Programs State/Local Earned Income Tax Credits (EITC) Prer-kindergarten (Univeral or Targeted) Head Start

http://www.countyhealthrankings.org/policies/clean-diesel-technology-fleet-transition-programs

Health Pyramid & Bucket 3

Presenter

Presentation Notes

Wraparound is an intensive, holistic method of engaging with individuals with complex needs (most typically children, youth, and their families) so that they can live in their homes and communities and realize their hopes and dreams. Since the term was first coined in the 1980s, "Wraparound" has been defined in different ways. It has been described as a philosophy, an approach, and a service. In recent years, Wraparound has been most commonly conceived of as an intensive, individualized care planning and management process. Wraparound is not a treatment per se. The Wraparound process aims to achieve positive outcomes by providing a structured, creative and individualized team planning process that, compared to traditional treatment planning, results in plans that are more effective and more relevant to the child and family

CDCFUNDED COMMUNITY HEALTH PROGRAMS: BUCKET 3 EXAMPLE

• PICH – Partnerships to Improve Community Health (38 Awardees) Supports mult i-sectoral community coalit ions in: Large Cities and Urban Counties (with populations of 500,000 or more) Small Cities and Counties (with populations between 50,000-499,999) American Indian tribes and Alaskan Native villages and tribal organizations

• REACH – Racial and Ethnic Approaches to Community Health (49 Awardees) 15-year old program Implementing locally tailored evidence- and practice-based population-

wide improvements in priority populations

Policies, Systems, Environmental Strategies Tobacco, Nutrition, Physical Activity, Community Clinical Linkages

Division of Community Health Programs

PICH & REACH Bucket 3 Example: Smoke Free Mult i-unit Housing

Issue: No safe level of exposure to second-hand smoke (SHS) Each year, among non-smokers* SHS exposure

• ~ 34,000 heart disease-related deaths• > 8,000 deaths from stroke• > 7,300 lung cancer deaths

MUH SHS spread from units, common areas, decks• Air vents, hallways, electrical outlets, cracks in ceilings & walls

U.S. residents of mult i-unit housing (MUH)**• ~ 70 of 80 million in MUH without smoke-free policies• ~ 45% higher cotinine levels among children living in MUH

Public Housing**• 88% MUH• 52% are older adults or disabled residents; 43% are children

* HHS 2014 **King et al. 2013

Smoke Free Policies: Indoor Air

Evidence-based, Cost Effect ive Intervent ion

Community Guide Systematic Review* 82 studies Strong evidence of effectiveness Across varied populations and settings

Short-term Health Benefits 50% reduction people exposed to SHS 3.8 percentage point increase smoking cessation 5.1% reduction CVD hospital admissions 20.1% reduction asthma-related hospital admissions

Economic Analysis Health care savings $700 - $1,297 per person $18M annual cost savings for MUH in CA Cleaning; Repairs; Maintenance

*Community Guide 2012

PICH & REACH Smoke Free Mult i-unit Housing: Est imated Impact

Year 1 Actual & Year 2 Projected Reach = 470,286 88,786 Low Income Residents 38,178 children 46,169 older or disabled adults

381,500 Private/Market Rate Residents 152,600 children

Public Health Impact 9,898 fewer smokers* 32 fewer CVD hospitalizations 115 fewer asthma hospitalizations

Annual Cost Savings $48.7M SHS-related healthcare $1.14M renovations $3.79M fire loss

* Cessation impacts & cost savings not included

Methods from King et al. 2013

Presenter

Presentation Notes

Total investment is recouped by the first 2 year societal savings from this one intervention

Take Away Messages Available sub-county data Identify minority, low income, & disparately impacted

populations Target upstream determinants for action Catalyze multi-sector partnerships

Exist ing frameworks (Collect ive Impact, Results Based Accountability) Establish shared vision Coordinate multisector actions Focus on quality of life/well being Monitor results focused indicators

Evidence-based intervent ion resource Impactful, measurable & cost effective Total populations and subpopulations Upstream determinants, contextual changes, high risk

population services

Collective Impact

Presenter

Presentation Notes

Total investment is recouped by the first 2 year societal savings from this one intervention

References

Guide to Community Preventive Services. Reducing tobacco use and secondhand smoke exposure: smoke-free policies. 2012. www.thecommunityguide.org/tobacco/smokefreepolicies.html

US Department of Health and Human Services. The Health Consequences of Smoking–50 Years of Progress: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014.

King BA, Peck RM, Babb SD. Cost-savings associated with prohibiting smoking in U.S. subsidized housing. American Journal of Preventive Medicine. 2013;44(6):631-634. doi: 10.1016/j.amepre.2013.01.024.

For more information please contact Centers for Disease Control and Prevention

1600 Clifton Road NE, Atlanta, GA 30333Telephone, 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348E-mail: [email protected] Web: www.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

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Vickie [email protected]

Divison of Community HealthNational Center for Chronic Disease Prevention and Health Promotion

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