Presentation on theme: "Using Data in Making Informed Decisions for Public Health Programs Rupa Sharma, M.Sc., MSPH Senior Epidemiologist Chronic Disease Epidemiology Section."— Presentation transcript:
Using Data in Making Informed Decisions for Public Health Programs Rupa Sharma, M.Sc., MSPH Senior Epidemiologist Chronic Disease Epidemiology Section Phone:
1: Presentation Outline Data Sources Measure burden and risk of disease Identify and prioritize populations at risk Plan effective and targeted interventions Evaluate program activities and outcomes Develop and / or change policies Requesting funds
2: Data Sources Disease registry Surveys / surveillance systems Vital statistics Hospital Discharge Data System Medicaid, Medicare claims data
3: Disease Surveillance Population based registries - Cancer, HIV Surveillance: The ongoing and systematic collection and analysis of data / information which leads to action being taken to prevent and control a disease. –The Public Health Information Network (PHIN) is standardized information systems which promotes efficient, integrated, and interoperable surveillance system at federal / state / local levels. Source:
4: Indicators for Chronic Disease Surveillance Source: In 1999, the Council of State and Territorial Epidemiologists (CSTE) released Indicators for Chronic Disease Surveillance to allow public health officials to uniformly define, collect, and report chronic disease data The Health Indicators report provides standard definitions for 73 indicators developed by epidemiologists and Chronic Disease program directors at the state / federal level. Specific indicators were selected because of their importance to Public Health and the availability of state-level data. BRFSS.
6: Monitor Disease Burden and Risk Morbidity: Impact on demographic groups, trends Mortality: Impact on demographic groups, trends Cost: Indirect and direct Risk Factors: Factors leading to the morbidity and mortality Knowledge of and attitude toward the disease Provider/Patient practices
7. Diabetes Prevalence in U.S by State, 2006 Source: Question: Have you ever been told by a doctor that you have diabetes?
8. Diabetes Prevalence in Arkansas and U.S, Source: Arkansas and U.S. BRFSS. Question: Have you ever been told by a doctor that you have diabetes?
9: Monitor Burden of Disease Direct cost - medical; Indirect cost - loss of work, disability and premature death –5,400 hospital discharges with primary Dx of diabetes - $ 87 millions charged in 2005 –27 per 100,000 deaths due to diabetes in 2005 –National cost of diabetes including direct and indirect costs - $192 B / $40 B Source: 2005 Arkansas Hospital Discharge Data;
10. Number of Hospital Discharges with Primary Dx of Diabetes, Total and Average Charges, Arkansas, Discharges / Charge ($) Discharges4,9545,6195,481 Total charge$55 M $73 M $87 M Average charge$11,180 $12,900 $15,896 Source: Arkansas Hospital Discharge Data
11. Age Adjusted Diabetes Mortality Rate Per 100,000 by Race in Arkansas, Source: data from
12. Trends of Diabetes Prevalence Among People With BMI ≥ 25 and < 25, Arkansas, Source: Arkansas BRFSS Data. Diabetes Q: Have you ever been told by a doctor that you have diabetes? Obesity Q: Weight classification by Body Mass Index (BMI)
13: Diabetes Care Practices (HP 2010) Source: a b 2006 Arkansas BRFSS; c Hospital Discharge Data. Objectives a Target 2010 bc Arkansas 2004 bc Arkansas Lower extremity amputations among persons with diabetes – (per 1,000) c HbA1C Test-at least two times a year among persons with diabetes (%) b Annual dilated eye examinations among persons with diabetes (%) b Annual foot examinations among persons with diabetes (%) b Self-blood-glucose-monitoring at least once a day among persons with diabetes (%) b
14: Identify / Prioritize Population at Risk of Having the Disease and Related Conditions Population groups with higher risk of having diagnosis of diabetes Geographic areas with higher diabetes prevalence Population groups with higher risk of developing diabetes complications Population groups with higher risk of dying of diabetes Geographic areas with higher diabetes related deaths
15: Diabetes Prevalence in Arkansas by Gender, 2006 Source: 2006 Arkansas BRFSS. Question: Have you ever been told by a doctor that you have diabetes?
16. Diabetes Prevalence in Arkansas by Race, 2006 Source: 2006 Arkansas BRFSS. Question: Have you ever been told by a doctor that you have diabetes?
17. Diabetes Prevalence in Arkansas by Age, 2006 Source: 2006 Arkansas BRFSS. Question: Have you ever been told by a doctor that you have diabetes?
18. Diabetes Prevalence in Arkansas by County, 2006 Source: 2006 Arkansas BRFSS. Question: Have you ever been told by a doctor that you have diabetes? BaxterBentonCarrollBoone Marion FultonRandolph Clay Greene LawrenceSharp Izard Stone Searcy Newton Madison Washington Independence CraigheadMississippi Poinsett Van BurenCleburne Pope JohnsonFranklin Crawford SebastianLogan Yell Scott White Conway Faulkner Jackson WoodruffCross St. Francis Lee PhillipsArkansas Monroe Prairie Lonoke Pulaski Perry PolkMontgomeryGarland Saline Critten- den Howard Sevier Pike Hot SpringGrant Jefferson Cleveland Dallas Clark LincolnDesha Chicot AshleyUnion Columbia Lafayette Miller Little River Hempstead Nevada Ouachita Calhoun Drew Bradley State average = 8.1 Sevier
19. Age Adjusted Diabetes Mortality Rate by Gender, U.S and Arkansas, 2005 Source: Arkansas Center for Health Statistics Online Query System (http://www.healthyarkansas.com/data/data.htmlhttp://www.healthyarkansas.com/data/data.html
20. Age Adjusted Diabetes Mortality Rates by Race, U.S and Arkansas, 2005 Source: 2005 data from
21. Age-Adjusted Diabetes Mortality Rates in Arkansas by County, 2005 Source: 2005 Arkansas Vital Statistics Data BaxterBenton Carroll Boone Marion FultonRandolphClay Greene LawrenceSharp Izard StoneSearcy Newton MadisonWashington IndependenceCraighead Mississippi PoinsettVan Buren Cleburne Pope JohnsonFranklin Crawford Sebastian Logan Yell Scott White Conway Faulkner Jackson Woodruff Cross St. Francis Lee Phillips Arkansas Monroe Prairie LonokePulaski Perry Polk MontgomeryGarland Saline Critten- den Howard Sevier Pike Hot SpringGrantJefferson ClevelandDallas Clark Lincoln Desha Chicot AshleyUnionColumbia Lafayette Miller Little River Hempstead Nevada Ouachita Calhoun Drew Bradley Mortality Rate per 100,000 State average = 26.6 State average: 26.6 per 100,000
22: Prioritizing Population Groups for Targeted Intervention 14-17% of adults age 55 years and over who have diabetes > 40 years old men / women with diabetes who live in Clay, Dallas, Chicot, Miller, Lafayette >40 years old men and women with diabetes in counties with no DSME programs >40 years old black men and women who are living in the counties with highest mortality rate
23: Planning Targeted Interventions Physical exercise and nutrition education ADA approved DSME in all counties CME and DSME for diabetes care providers through ACIC Implement *Chronic Care Model Media campaign, flu vaccine campaign *see next slide
24: ADA Approved DSME Programs Sebastian Independence Little River Source: American Diabetes Association 2006
25: Distribution of Ophthalmologists Source: Arkansas Center for Health Statistics 2006
26: Distribution of Optometrists Source: Arkansas Center for Health Statistics 2006
27: Program Evaluation Process Evaluation: Document the process of diabetes prevention, intervention and control activities Outcome Evaluation: Determine health and other outcome indicators and measure the outcomes of prevention, intervention efforts
28: Process Evaluation Measures No. of individuals who listened to PSA No. of diabetes care providers receiving CME / DSME trainings No. of clinics implementing Chronic Care Model No. of new DSME programs approved by ADA No. of dieticians, nurses providing DSME No. of Wal-Mart gift certificates distributed No. of transportation assistance provided No. of presentations, brochures given out
29: Process Evaluation measures No. of patients receiving HbA1c tests 2/yr No. of patients with HbA1c <7 No. patients with blood pressure <130/80 No. of patients with LDL cholesterol <100 No. of patients with foot exam 1 / yr No. of patients with dilated eye exam 2 / yr No. of patients monitoring blood glucose 1 / day
30. Monitor Process: Trends of HbA1c Level < 7 among Diabetes Patients, 2007 Source: 2007 ACIC Participating Clinics’ Data
31: Benchmarks for Outcome Evaluation Qualitative measure – diabetes knowledge better than before Quantitative measure – 75 counties with DSME compared to 24 counties. Start with baseline data, provide intervention and compare with follow-up data after intervention Establish specific indicators for measure e.g. change in service delivery, knowledge, health outcomes
32: Outcome Evaluation No. of patients visiting ACIC clinics due to PSA Change in provider care practices as a result of CME and DSME training Change in the self-care behavior of people with diabetes after attending DSME Decrease in diabetes related hospitalizations Decrease in diabetes related ER visits Change in ACIC clinic patients’ health outcomes
33: Develop / Change Health Policies DPCP Pilot Study of Medicaid Recipients The goal is for Medicaid to reimburse DSME. Provide evidence to Medicaid providers on the effectiveness of DSME among Medicaid recipients in reducing diabetes related complications, leading to reduction in the cost of diabetes treatment.
34: Develop / Change Health Policies DPCP Pilot Study of Medicaid Recipients 13 hours of education was provided to 212 Medicaid study participants in three visits / year All areas of DSME were covered Measured changes in clinical indicators, health care use / expenditure
35: Develop / Change Health Policies DPCP Pilot Study of Medicaid Recipients Decrease in patients’ HbA1c levels Fewer hospitalizations, emergency room / outpatient department visits 3 years projection of cost savings - $415 per program completer 10 years projection - 12% fewer CHD, 15% fewer micro-vascular disease
36: Develop / Change Health Policies Results shared with Medicaid director and other stakeholders Stakeholders exploring ways to sustain DSME for Medicaid recipients
37: To Make Informed Decision Use good quality and representative data Have staff with proper skills and expertise Use proper plan, methodology and design Seek partnership and collaboration when resources are limited Make use of the knowledge and experience of other agencies or states Get technical help from funding agencies