Epe talk for Supervisors

EPEYour partners in data-driven, evidence based public health

Draft08/30/11

Colorado Department of Public Health and EnvironmentStrategic Map: 2011-2015

Align Priorities and Resourcesto Improve and Sustain Public Health

and Environmental Quality

Use Performance-Based Measures and Evaluationto Continuously Improve Effectiveness and Prioritize Resources

Valueand ShareInnovation

Meet Regulatoryand StatutoryRequirements

Align EmployeeGoals and Roleswith Department

PrioritiesProvide ProfessionalDevelopment and

AdvancementOpportunities

CommunicateWinnable Battles to

Employees and IdentifyLinkages to their Work

ChampionCollaboration Among

Programs, Departmentsand Partners

Educate ExternalPartners on

Winnable Battles

StrengthenCross-Departmental

Collaboration/Integration

ConsistentlyApply Department

Policies andProceduresInfluence

State HR andProcurement

Policies

Use New and AlternateTechnologies to

OptimizeEmployee Performance

Strengthen Internaland External

Communication

Recognize andReward Employee

Contributions

Keep Up withthe Speed of

Business

SecureSustainable,

Diversified Funding

Promote Health Equity and Environmental Justice

Develop aLeadership

Succession Plan

Promote the Valueof Public Health and

EnvironmentalProtection

Actively Participateas a Partner

in the Community

Create and SupportSustainable, Healthy

Communities

Create a MoreEfficient, Effective,Customer-Oriented

Department

Strengthen theIntegrated Public

and EnvironmentalHealth System

Foster a Competent,EmpoweredWorkforce

PromoteProgrammatic

Excellence

Achieve TargetedImprovements in

Colorado’sWinnable Battles

A B D EC

Implement thePublic Health

Improvement Plan

PromotePublic Health and

EnvironmentalCareers

EstablishEvidence-Based,

Data-DrivenStrategies

DeliverExcellent

Customer Service

Develop andImplement Strategies

to AchieveWinnable Battles

1

AchieveClear, Specific,

Measurable Goals

Attract and RetainTalented and

Diverse Employees

Engage Partners in Programmatic

and PolicyDecision-Making

Employ LeanMethodologies to Improve Business

Processes

Promote PoliciesThat Support

Winnable Battles2

3

4

5

6

7

F

G

Topics

1. Insights2. A bit about Epi and Eval3. Insights from Karen T.4. 3 examples of collaboration with

EPE5. Discussion: What would we like

to see in a collaborative business model and working relationship with EPE and programs?

Good TimingNew CDPHE strategic mapNew leadership in CDPHE & PSDNew Colorado Winnable BattlesOpportunity to address questions

Insights from EPE Strategic Planning…

Data driven, evidence-based public health action, with every program, every time

Current funding and operations model of EPE is a barrier

Our work is more useful when we’ve used a more collaborative model

Steps of Evidence-Based Public Health

Brownson, RC; Fielding JE; Maylahn CM. Ann. Rev. Public Health 2009.30:189


1. What are the

community needs, assets, and values?

3. What

potential

strategies are

relevant?4. What

potential strategies

are effective?

In Colorado?

5. What effective strategie

s are priorities

?

Epi Questions2. What are the

largest problems in the

population? Who are at high

risk? What is contributing to

health?

Epidemiology Unit in EPE

Renee Calanan

Stephanie Kuhn

Sue Ricketts,demographer

Ashley Juhl

Indira GujralDeclan Calanan

DOB: 9/29/11

Epi Unit contributesBasic public health scienceCritique of existing evidence

◦Did the research really work?◦Was the research done well enough to act

on the findings?Assessment of the relationships

among assets, protective factors, behaviors, and health in the population◦Systems thinking

Interpretation of real-world dataMore details at: www.cste.org/competencies.asp

Epi Unit contributes

Expertise in large data sources:From Health Statistics Section ~◦BC, PRAMS, CHS, BRFSS, DC

Other state or national sources: WFRS, MEPS, CHA-HDD, MDS, Census, SAIPE, ACS, BSS, YRBS, CIIS,CRCSN, COVDRS,CFPS, FARS, TAR, CSAP, CBI, CDE, PedNSS, NSCH, NS-CSHCN, NHANES, NHIS, TABS, CHKST, NYTS, NIS, NSFG, NVSS, NSDUH, NSRE, HIV/STD, CHP+, COHS, HEDIS, Medicaid, NDI, SHP, HCUP, ESRD

We answer the big questions.What matters?What might we do about what

matters?

Tooth Loss, Age 45-64, BRFSS 2010

The SURVEYFREQ Procedure

Number of Strata 42Number of Observations 11663

Sum of Weights 3854905

Weighted Std Dev of Std Err ofFrequency Wgt Freq Percent

1+ 2287 559262 12176 535391 583132 42.931 0.8478 41.2689 44.5931None 2902 743438 12375 719177 767699 57.069 0.8478 55.4069 58.7311Total 5189 1302700 10762 1281602 1323797 100

Tooth Loss by geography, Age 45-64, BRFSS 2010




Weighted Std Dev of Std Err of Row Std Err ofFrequency Wgt Freq Percent Percent Row Percent

rural 1+ 743 104221 3896 96582 111859 8.0262 0.2973 7.4434 8.6091 49.0887 1.552 46.0461None 757 108090 3525 101180 115001 8.3242 0.2742 7.7866 8.8619 50.9113 1.552 47.8686Total 1500 212311 3447 205552 219069 16.3505 0.2648 15.8313 16.8696 100

urban 1+ 1538 454126 11564 431455 476796 34.9731 0.8206 33.3644 36.5818 41.8091 0.9693 39.9088None 2128 632063 11915 608704 655422 48.6764 0.8222 47.0645 50.2884 58.1909 0.9693 56.2906Total 3666 1086189 10403 1065795 1106582 83.6495 0.2648 83.1304 84.1687 100

Total 1+ 2281 558346 12169 534489 582203 42.9993 0.8498 41.3333 44.6654None 2885 740153 12361 715920 764386 57.0007 0.8498 55.3346 58.6667Total 5166 1298499 10752 1277420 1319578 100

Data Summary

Table of rmvteeth2cTooth loss Frequency 95% Confidence Limits

for Wgt FreqPercent 95% Confidence Limits

for Percent

for Percent95% Confidence Limits

for Row Percent

Frequency Missing = 6497


Data Summary

Table of region2 by rmvteeth2cRegion Tooth Loss Frequency 95% Confidence Limits


Tooth Loss by Poverty Level, Age 45-64, BRFSS 2010





0-250% FPL 1+ 814 200409 8597 183554 217263 15.3841 0.6357 14.1378 16.6304 57.4797 1.7535 54.0422None 550 148251 7939 132688 163814 11.3803 0.5906 10.2224 12.5382 42.5203 1.7535 39.0827Total 1364 348660 11130 326840 370480 26.7644 0.7889 25.2177 28.3111 100

251+% FPL 1+ 1473 358853 10370 338523 379183 27.5469 0.7685 26.0403 29.0534 37.614 0.9589 35.7343None 2352 595187 11479 572683 617691 45.6887 0.8484 44.0255 47.352 62.386 0.9589 60.5062Total 3825 954040 11854 930802 977278 73.2356 0.7889 71.6889 74.7823 100

Total 1+ 2287 559262 12176 535391 583132 42.931 0.8478 41.2689 44.5931None 2902 743438 12375 719177 767699 57.069 0.8478 55.4069 58.7311Total 5189 1302700 10762 1281602 1323797 100

Tooth Loss, poverty and geography, Age 45-64, BRFSS 2010





rural 1+ 330 47420 3277 40995 53845 13.6206 0.9242 11.8087 15.4325 67.1155 2.6351 61.9495

95% Confidence Limitsfor Row Percent


Data Summary

Data Summary

Table of poverty by rmvteeth2cpoverty Tooth Loss Frequency 95% Confidence Limits


for Percent


Table of region2 by rmvteeth2cControlling for poverty=0-250% FPL

Region Tooth Loss Frequency 95% Confidence Limitsfor Wgt Freq

Percent 95% Confidence Limitsfor Percent

None 166 23234 2082 19152 27316 6.6737 0.6036 5.4904 7.8569 32.8845 2.6351 27.7185Total 496 70654 3672 63455 77853 20.2943 1.0415 18.2525 22.3361 100

urban 1+ 483 152793 7952 137203 168382 43.8873 1.738 40.48 47.2946 55.0617 2.0815 50.981None 382 124701 7657 109689 139713 35.8184 1.7382 32.4109 39.226 44.9383 2.0815 40.8576Total 865 277494 10511 256887 298101 79.7057 1.0415 77.6639 81.7475 100

Total 1+ 813 200213 8595 183363 217062 57.5079 1.7555 54.0664 60.9494None 548 147935 7935 132379 163491 42.4921 1.7555 39.0506 45.9336Total 1361 348148 11126 326337 369959 100


rural 1+ 413 56801 2891 51133 62469 5.9768 0.304 5.3809 6.5728 40.0975 1.7971 36.5744None 591 84856 3296 78394 91318 8.9289 0.3469 8.2488 9.6089 59.9025 1.7971 56.3795Total 1004 141657 3465 134864 148449 14.9057 0.3641 14.1918 15.6196 100

urban 1+ 1055 301333 9968 281791 320874 31.7075 0.9373 29.8701 33.545 37.2616 1.0857 35.1332None 1746 507362 11041 485718 529006 53.3868 0.9459 51.5323 55.2412 62.7384 1.0857 60.6099Total 2801 808695 11453 786241 831148 85.0943 0.3641 84.3804 85.8082 100

Total 1+ 1468 358134 10363 337817 378450 37.6843 0.9616 35.7991 39.5696None 2337 592218 11465 569741 614694 62.3157 0.9616 60.4304 64.2009Total 3805 950351 11844 927132 973570 100

Tooth Loss by income, Age 45-64, BRFSS 2010





Less than $15k 1+ 240 44144 3561 37162 51125 3.7447 0.304 3.1488 4.3407 61.4118 3.7334 54.0925None 116 27738 3690 20504 34972 2.353 0.3112 1.743 2.963 38.5882 3.7334 31.2689Total 356 71882 5054 61973 81790 6.0977 0.4274 5.2598 6.9357 100

$15k to less than $25k 1+ 318 71875 5077 61922 81828 6.0972 0.4265 5.2609 6.9334 61.0182 2.7224 55.681

Table of region2 by rmvteeth2cControlling for poverty=251+% FPL

95% Confidence Limitsfor Percent


Region Tooth Loss Frequency 95% Confidence Limitsfor Wgt Freq

Percent


Data Summary

Table of _INCOMG by rmvteeth2cIncome Tooth Loss Frequency 95% Confidence Limits


for Percent

None 206 45918 4015 38046 53789 3.8952 0.3403 3.228 4.5624 38.9818 2.7224 33.6445Total 524 117792 6353 105338 130247 9.9924 0.5331 8.9472 11.0375 100

$25k to less than $35k 1+ 224 51145 4381 42557 59733 4.3387 0.3695 3.6143 5.063 57.2364 3.1149 51.1297None 175 38213 3496 31358 45067 3.2416 0.2968 2.6597 3.8235 42.7636 3.1149 36.657Total 399 89358 5519 78538 100178 7.5803 0.4656 6.6675 8.4931 100

$35k to less than $50k 1+ 338 79059 5313 68644 89474 6.7066 0.4473 5.8296 7.5836 51.1456 2.421 46.3993None 344 75517 5009 65698 85337 6.4062 0.4238 5.5753 7.237 48.8544 2.421 44.108Total 682 154577 7105 140647 168507 13.1128 0.5963 11.9438 14.2817 100

$50k or more 1+ 961 264709 9821 245456 283962 22.4553 0.7897 20.9072 24.0035 35.5211 1.1324 33.3012None 1757 480507 11188 458572 502441 40.7615 0.8861 39.0244 42.4987 64.4789 1.1324 62.2589Total 2718 745216 12440 720828 769604 63.2169 0.8578 61.5352 64.8986 100

Total 1+ 2081 510932 11717 487961 533903 43.3425 0.8972 41.5836 45.1014None 2598 667892 11801 644757 691028 56.6575 0.8972 54.8986 58.4164Total 4679 1178825 10347 1158541 1199109 100

Tooth Loss by race/ethnicity, Age 45-64, BRFSS 2010





White - Non-Hispanic 1+ 1793 395694 9930 376228 415160 30.6808 0.7688 29.1735 32.188 39.1622 0.8923 37.4129None 2553 614705 10995 593150 636259 47.6621 0.8526 45.9906 49.3336 60.8378 0.8923 59.0886Total 4346 1010399 10456 989901 1030897 78.3429 0.8083 76.7583 79.9275 100

Black - Non-Hispanic 1+ 49 22413 4071 14432 30394 1.7378 0.3131 1.124 2.3516 62.3597 6.5369 49.5446None 29 13528 2829 7983 19074 1.0489 0.2186 0.6203 1.4776 37.6403 6.5369 24.8252Total 78 35941 4932 26273 45609 2.7867 0.378 2.0458 3.5277 100

Hispanic 1+ 310 104479 7088 90583 118375 8.1009 0.5329 7.0563 9.1456 56.277 2.628 51.1251None 218 81172 6413 68600 93745 6.2938 0.4858 5.3415 7.2461 43.723 2.628 38.5711Total 528 185651 9306 167407 203896 14.3948 0.6802 13.0613 15.7282 100

Other race only, Non-Hispanic 1+ 63 21332 3326 14812 27851 1.654 0.2564 1.1514 2.1566 51.1982 6.2902 38.8667None 48 20333 4003 12487 28180 1.5766 0.3077 0.9734 2.1798 48.8018 6.2902 36.4704


Data Summary

for Row Percent

Table of _RACE_G by rmvteeth2cRace/Ethnicity Tooth Loss Frequency 95% Confidence Limits



Total 111 41665 5190 31490 51840 3.2306 0.3964 2.4536 4.0076 100Multiracial, Non-Hispanic 1+ 39 9512 1876 5834 13190 0.7376 0.1454 0.4525 1.0227 59.2391 8.1239 43.3128

None 25 6545 1775 3065 10025 0.5075 0.1375 0.238 0.777 40.7609 8.1239 24.8345Total 64 16058 2574 11012 21103 1.2451 0.1992 0.8545 1.6357 100

Total 1+ 2254 553430 12136 529639 577221 42.9111 0.8528 41.2391 44.583None 2873 736284 12307 712157 760411 57.0889 0.8528 55.417 58.7609Total 5127 1289714 10707 1268723 1310705 100

Tooth Loss by gender, Age 45-64, BRFSS 2010





male 1+ 999 285720 10633 264874 306566 21.9329 0.7592 20.4445 23.4213 43.978 1.312 41.4059None 1178 363969 11584 341259 386679 27.9396 0.8118 26.3481 29.5311 56.022 1.312 53.45Total 2177 649689 14204 621843 677535 49.8725 0.8657 48.1753 51.5696 100

female 1+ 1288 273542 8486 256906 290177 20.9981 0.6592 19.7057 22.2905 41.8893 1.0918 39.7489None 1724 379469 9249 361338 397601 29.1294 0.74 27.6787 30.5802 58.1107 1.0918 55.9703Total 3012 653011 10501 632424 673598 50.1275 0.8657 48.4304 51.8247 100

Total 1+ 2287 559262 12176 535391 583132 42.931 0.8478 41.2689 44.5931None 2902 743438 12375 719177 767699 57.069 0.8478 55.4069 58.7311Total 5189 1302700 10762 1281602 1323797 100

Tooth Loss by education, Age45-64, BRFSS 2010


Number of Strata 42


Data Summary

Table of SEX by rmvteeth2c95% Confidence Limits


for Row PercentSEX Tooth Loss Frequency 95% Confidence Limits

for Wgt FreqPercent


Data Summary

Number of Observations 11663Sum of Weights 3854905


Did not graduate High School 1+ 136 39425 4325 30947 47904 3.0304 0.329 2.3854 3.6754 72.5607 4.1631 64.3992None 46 14909 2629 9754 20064 1.146 0.2014 0.7512 1.5407 27.4393 4.1631 19.2777Total 182 54334 5028 44478 64190 4.1763 0.381 3.4295 4.9232 100

Graduated High School 1+ 705 165005 7376 150544 179465 12.6829 0.5575 11.59 13.7759 56.6558 1.8072 53.1128None 494 126236 6894 112721 139751 9.703 0.5211 8.6814 10.7246 43.3442 1.8072 39.8013Total 1199 291241 9637 272349 310133 22.3859 0.7132 20.9879 23.784 100

Attended College or Technical School 1+ 726 168565 7618 153630 183501 12.9566 0.5769 11.8257 14.0875 47.1082 1.64 43.893None 776 189260 8104 173373 205148 14.5473 0.6099 13.3516 15.7431 52.8918 1.64 49.6767Total 1502 357826 10448 337342 378309 27.5039 0.7702 25.994 29.0138 100

Graduated from College or Technical School

1+ 719 186140 8247 169973 202307 14.3074 0.6197 13.0925 15.5224 31.148 1.1956 28.804

None 1579 411459 10458 390956 431962 31.6264 0.7856 30.0862 33.1665 68.852 1.1956 66.5081Total 2298 597599 11797 574473 620725 45.9338 0.8503 44.2668 47.6009 100

Total 1+ 2286 559135 12171 535274 582996 42.9773 0.8486 41.3137 44.641None 2895 741865 12370 717615 766115 57.0227 0.8486 55.359 58.6863Total 5181 1301000 10755 1279916 1322083 100

Tooth Loss by Smoking Status, Age 45-64, BRFSS 2010





current 1+ 583 135126 6736 121920 148332 10.4091 0.5141 9.4013 11.4169 67.5201 1.9882 63.6224None 262 65001 4719 55749 74253 5.0072 0.3631 4.2954 5.719 32.4799 1.9882 28.5822Total 845 200127 7974 184495 215759 15.4163 0.6074 14.2256 16.607 100

former 1+ 808 195586 8249 179414 211759 15.0665 0.6219 13.8473 16.2857 50.3608 1.5874 47.2487


for Row Percent


Table of _EDUCAG by rmvteeth2cEducation Tooth Loss Frequency 95% Confidence Limits



Data Summary

Table of smokestat by rmvteeth2cSmoking Statis Tooth Loss Frequency 95% Confidence Limits


for Percent

EPE provides visual context

60%

23%

14%

63%

19%15%

62%

22%

14%

70%

17%

10%

54%

28%

14%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Yes No No partner/not sexually active

Are you or your partner doing anything to keep from getting pregnant? Colorado, BRFSS, 2002, 2004, 2005, 2006 and 2009

2002

2004

2005

2006

2009

Intended Pregnancy by Age, Colorado, 2009

Age 15-19 Age 20-24 Age 25-34 Age 35+0%

20%

40%

60%

80%

100%

26%

43%

70% 73%Healthy People 2020 target

Sexual Behavior Among Colorado Adults

A CLOSER LOOK: Long Acting Reversible Contra-ceptive Methods (‘02, ’04, ‘05, ‘06, ‘09)

2.4%

3.2%

5.5%

6.7%

10.8%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

LARC METHODS

2002 2004 2005 2006 2009

Funding to Delegates November 2008

Data Source: Behavioral Risk Factor Surveillance System, Colorado Department of Public Health and Environment

EPE provides written context for issues

“Unintended pregnancy is defined as one that is either unplanned or mistimed (occurred

soonerthan desired). Women who are intentional about becoming pregnant are more likely to

startprenatal care early, and to adopt healthy behaviors during pregnancy. The national

HealthyPeople 2010 Objective 9‐1 is to increase the proportion of intended pregnancies to 70

percentof all births.

“ The highest rates of unintended pregnancies are found in two groups: low-income women and young women. In 2008, only 42 percent of births to women on Medicaid were intended, compared to 72 percent of births to women not receiving Medicaid. 6,viii For younger women (ages 15-19) during the same year, only an estimated 36 percent of births were intended. Similarly, only 41 percent of births to women ages 20-24 years old were intended. Intended pregnancies increased significantly after age 24, where 70 percent of births to women ages 25-34, and 67 percent of births to those ages 35 and older were reportedly intended. 6 Reducing unintended pregnancies among young and low-income women would help Colorado reach the Healthy People 2010 objective for intended pregnancy.”

Excerpts from Chapter 2, The Health Status of Colorado’s Maternal and Child Health Population, 2010

Evaluation in EPE

Arthur McFarlaneJulie Graves

Mario Rivera Kerry Thomson

External Evaluation Oversight

Carsten Baumann

Internal Evaluation

Evaluation, defined

“Ultimately, evaluation is concerned with asking questions about issues that arise out of everyday practice. It is a means for gaining a better understanding of what we do and the effects of our actions in the context of society and the work environment.”

--Russ-Eft, H. and Preskill, D. (2005)

Evaluation

Evaluation focuses before

the tree sprouts!

Scan of an Evaluator’s Brain

What is an Evaluator thinking?!

Why…?Why…?

When…?How…?

Under what circumstance…?

Moredetail & context

please!

Evaluation Questions


Process Evaluation

How much was provided and with what consistency?

Did I reach the target population?

Am I hitting my benchmarks?

Are my strategies being carried out as

planned?

Formative Evaluation

How can we improve the program?

How effective are my

collaborative partnerships?

Outcome Evaluation

Were my strategies effective in

achieving the desired outcomes?

Needs Assessment

Where are the gaps in the community?

What does the community feel is

important?

Evaluability Assessment

What type of evaluation is my program ready for based on

what we know?

The Evaluation Unit Contributes:Collaboration on logic modelsHelping write SMART objectivesIdentifying process and outcome

measures to determine if you are meeting goals and objectives

Collaboration to decide on an appropriate evaluation focus and design (Evaluability Assessment)

Design of data collection forms/procedures

Analysis and interpretation of evaluation data

Collaboration is Key

= planning & executing the right analysis, intervention, & evaluation at the right time!

Understanding of the Health Problem

Content knowledge and knowledge about strategies

Information about the community

Stakeholder relationships and needs

Understanding of the Health Problem

Understanding of data and the complex

determinants of health

Scientific method

Objectivity

Outcome-orientation

SMART Objectives

+

EPE Programs

Collaboration in PSDWe are one division. We are working in horizontal

teams, because successful public health is multi-disciplined.◦3-way partnership: Epi, Eval,

ProgramsLet’s together work on a

collaborative business model for EPE.

Insights from Karen…

Youth Access to Tobacco

Celeste Schoenthale

r

Maternal, Infant and Early Childhood Home Visiting Program

Five federal applications, including statewide needs assessment, state plan for home visiting and a competitive “expansion” application, were submitted

EPE = critical program partner from planning to execution◦ RFI to identify existing home visiting programs◦ Needs assessment to identify at-risk communities◦ Prioritization for investments based on county strengths

and needs◦ Funding formula to distribute money to prioritized

counties◦ Logic Models and Smart Objectives◦ Data collection plan for 6 Benchmark areas with 36

constructs◦ CQI plan and implementation ◦ Working closely with our partners, the EB model reps◦ Highly academic evaluation plan developed for

competitive application◦ Will analyze and help interpret data; prepare reports

MCH Local Planning Process

Gina Febbraro

Facilitated DiscussionHow can we create a better

collaboration?What would you like to see in a

collaborative business model for EPE?

Epe talk for Supervisors

Health & Medicine

Transcript of Epe talk for Supervisors