# Presentation: 741532

## Tracking Healthy People 2010’s Leading Health Indicators at the County Level

**Case Study ~ Anne Arundel County, MD**

- Suzanne E. Proctor, MSPH
- National Center for Health Statistics
- Centers for Disease Control and Prevention

## Leading Health Indicators (LHIs)

-  	10 indicators (22 measures) that reflect major 	health priorities
-  	Provide a ‘snapshot’ of the nation’s health at 	regular intervals
-  	Selected based on their ability to motivate 	action
-       Measurable at the national and State levels

## Leading Health Indicators and Measures

**Physical Activity**
- Moderate physical activity in adults
- Vigorous physical activity in adolescents
**Overweight and Obesity****	**
- Obesity in adults
- Overweight/obesity in children/adolescents
**Tobacco Use****	****       **
- Cigarette smoking by adults
- Cigarette smoking by adolescents
**Substance Abuse**
- Alcohol and illicit drug use by adolescents
- Illicit drug use by adults
- Binge drinking by adults

**     ****Responsible Sexual Behavior**
-      Abstinence or condom use by adolescents
-      Condom use by adults

**Mental Health**
- Treatment of adults with recognized depression
**Injury and Violence**
- Deaths from motor vehicle crashes
- Homicides
**Environmental Quality**
- Ozone pollution exposure
- Environmental tobacco smoke exposure
**Immunization**
- Fully immunized children aged 19-35 months
- Influenza vaccination in high-risk adults
- Pneumococcal vaccination in high-risk adults
**Access to Care**
- Persons with health insurance
- Source of ongoing care
- Early prenatal care

## Data Sources for the LHI Measures

**Same national and State source:**	   **	****         ****	**** Number of measures**
-     National Household Survey on Drug Abuse    		      	  4
-     National Vital Statistics System 				  3
-     Youth Risk Behavior Surveillance System        	  		  3
-     Aerometric Information Retrieval System         	  		  1
-     National Immunization Survey				  1
**BRFSS (State) and different national source:**
-     National Health Interview Survey                      			  6
-     National Health and Nutrition Examination Survey 		  1
-     National Survey of Family Growth				  1
**YRBSS (State) and different national source: **
-     National Health and Nutrition Examination Survey                	  1
**National source only:**
-     National Health and Nutrition Examination Survey		  1

## Behavioral Risk Factor Surveillance System (BRFSS)

-    	Monthly telephone survey jointly conducted by 	CDC and the States (1984 – present)
-    	Survey assesses behavioral risk factors in adults 	18 years and over
-  	50 States, D.C., PR, and VI participate
-    	States conduct 1,500-7,500 interviews per year

## BRFSS Data Availability

-    	National and State data are published by CDC 	each year
-  	However, sub-State data are generally not 	available
-  	State sample sizes generally too small to produce	reliable estimates
-  	Even when sub-State estimates can be produced, 	analysis of subpopulations is seldom possible

## Methods for Obtaining Sub-State Data

-    	Aggregate data for several years
-  	Aggregate data for several geographic areas
-  	Conduct a special sub-State level BRFSS
-   	Produce synthetic estimates based on State-	level survey data

## Synthetic Estimation

**Basic concept: **
- Estimate the prevalence of a	health behavior/outcome
- in a sub-State population based on the prevalence of the
- behavior/outcome in the State population, *assuming*
- individuals with similar characteristics behave similarly in
- both populations

## Model-Based Estimation

- Use multivariate regression modeling to estimate the
- prevalence of health behaviors/outcomes at the local
- level
**Method:****   **
- Formulate a regression model using State-level data
- from a sample survey (e.g. BRFSS).
- Use the model to predict the prevalence of the health
- behavior/outcome in the sub-State population.
** **

## Model-Based Estimation

**Steps:****   **
-  	Specify a model that includes predictor variables 	related to the health behavior/outcome
- 		-	Limited to variables that are included in the sample 			survey *and *are available at the sub-State level
-  	Fit the model using the State-level survey data
- (3) 		Plug sub-State demographic data into the fitted 	model to obtain an estimate of the prevalence of the 	health behavior/outcome in the sub-State population** **

## Scenario:

Anne Arundel County, MD conducted the BRFSS in 2001

 	1,112 residents interviewed

 	25.7% response rate

 	Prevalence estimates were produced for a number of 	health risk factors ... including 6 of the LHI measures

Objective:

Compare survey- and model- generated estimates for the
6 LHI measures

- Anne Arundel County, MD conducted the BRFSS in 2001
-  	1,112 residents interviewed
-  	25.7% response rate
-  	Prevalence estimates were produced for a number of 	health risk factors ... *including 6 of the LHI measures*
**Objective:**
- Compare survey- and model- generated estimates for the
- 6 LHI measures

- Case Study ~ Anne Arundel County, MD

## BRFSS Results for Anne Arundel County and Maryland State, 2001

| Leading Health Indicator Measure |         Prevalence |
| --- | --- |
|  |    AAC            MD State |
| 22-02.     Moderate physical activity in adults |   25.4%                   25.2% |
| 27-01a.   Cigarette smoking by adults |   34.7%                   21.1% |
| 14-29a.   Influenza vaccination in high-risk adults |   72.3%                   67.3% |
| 14-29b.   Pneumococcal vaccination in high-risk adults |   65.0%                   62.3% |
| 01-01.     Persons with health insurance |   95.6%                   89.7% |
| 01-04c.   Specific source of ongoing care |   86.7%                   85.5% |
|  |  |

## Model-Based Estimation ~ Cigarette Smoking

**    ****Step 1a: Specify the regression model **
*                                              **1*
*            **1 + e **– [**α** **+ B**1** (X**1**) + B**2** (X**2**) + B**3** (X**3**) + B**4** (X**4**) + B**5** (X**5**)** +** **...]*
**   ****Step 1b: Select variables for inclusion**
*                                            **1*
*          **1 + e **– [**α** **+ B**1** (Age) + B**2** (Sex) + B**3** (Education Level) + B**4** (Race/Ethnicity)]*

-  P =

- Probability     of Smoking

- =

## Model-Based Estimation ~ Cigarette Smoking

**Step 1c: Define variables**
**Variable ****	****	****	****Category Definitions**
- Smoking (outcome)			1 = smoker, 0 = nonsmoker
- Age
-      Indicator variable 1		1 = 18-34, 0 = otherwise
-      Indicator variable 2		1 = 35-54, 0 = otherwise
- Sex					1 = male, 0 = female
- Education Level			1 =  _<_ HS graduate, 0 =  > HS graduate
- Race/Hispanic origin
-      Indicator variable 1		1 = Black only not Hispanic, 0 = otherwise
-      Indicator variable 2		1 = Other not Hispanic, 0 = otherwise
-      Indicator variable 3		1 = Hispanic, 0 = otherwise

## Model-Based Estimation ~ Cigarette Smoking

**Step 2a: Fit the “full” model using the State-level **
**survey data**

- The LOGISTIC Procedure
- Analysis of Maximum Likelihood Estimates
-                                                     	 Standard          Wald
- Parameter    DF          Estimate         	 Error  	           Chi-Square	          Pr > ChiSq
- Intercept	      1 	     -2.4556	 0.1043	           554.2812             <0.0001
- AGE1	      1  	      0.8933	 0.1141	             61.3077             <0.0001
- AGE2	      1 	      0.8965 	 0.1012	             78.5209             <0.0001
- SEX	      1	      0.2589       	 0.0787 	             10.8235	             0.0010
- EDUC	      1  	      0.8560       	 0.0796 	           115.7836	           <0.0001
- RACE1	      1  	     -0.2042       	 0.1053    	 3.7643 	             0.0524
- RACE2	      1  	     -0.3561       	 0.2076       	 2.9430 	             0.0863
- RACE3	      1 	     -0.2760       	 0.2087        	 1.7486  	             0.1861
- Full model: -2 Log L  = 4138.905

## Model-Based Estimation ~ Cigarette Smoking

**Step 2b: Eliminate variables that are not significantly**
**associated with the outcome**

- The LOGISTIC Procedure
- Analysis of Maximum Likelihood Estimates
-                                                     	 Standard          Wald
- Parameter    DF          Estimate         	 Error  	           Chi-Square	          Pr > ChiSq
- Intercept	      1 	     -2.4556	 0.1043	           554.2812             <0.0001
- AGE1	      1  	      0.8933	 0.1141	             61.3077             <0.0001
- AGE2	      1 	      0.8965 	 0.1012	             78.5209             <0.0001
- SEX	      1	      0.2589       	 0.0787 	             10.8235	             0.0010
- EDUC	      1  	      0.8560       	 0.0796 	           115.7836	           <0.0001
- RACE1	      1  	     -0.2042       	 0.1053    	 3.7643 	             0.0524
- RACE2	      1  	     -0.3561       	 0.2076       	 2.9430 	             0.0863
- RACE3	      1 	     -0.2760       	 0.2087        	 1.7486  	             0.1861
- Reduced model: -2 Log L  = 4198.802   Likelihood Ratio Test: *X**2 **= 60*, df = 7, p < 0.001

- ?

## Model-Based Estimation ~ Cigarette Smoking

**Final model:**

- The LOGISTIC Procedure
- Analysis of Maximum Likelihood Estimates
-                                                     	 Standard          Wald
- Parameter    DF          Estimate         	 Error  	           Chi-Square	          Pr > ChiSq
- Intercept	      1 	     -2.4556	 0.1043	           554.2812             <0.0001
- AGE1	      1  	      0.8933	 0.1141	             61.3077             <0.0001
- AGE2	      1 	      0.8965 	 0.1012	             78.5209             <0.0001
- SEX	      1	      0.2589       	 0.0787 	             10.8235	             0.0010
- EDUC	      1  	      0.8560       	 0.0796 	           115.7836	           <0.0001
- RACE1	      1  	     -0.2042       	 0.1053    	 3.7643 	             0.0524
- RACE2	      1  	     -0.3561       	 0.2076       	 2.9430 	             0.0863
- RACE3	      1 	     -0.2760       	 0.2087        	 1.7486  	             0.1861

## Step 3: Plug sub-State demographic data from the 
Census into the final model

**Census into the final model**

- Model-Based Estimation ~ Cigarette Smoking

*                                                                   **1*
*                    **1 + e** **– [-2.4** **+  0.89(Age1) + 0.90(Age2)** **+ 0.26(Sex) + 0.86(Educ) - 0.20 (Race1) – **	**	**	**	**	**                      ** 0.36 (Race2) – 0.28(Race3)]*

- Probability     of Smoking

- =

- Probability of smoking = 40.4%

## Comparison of Survey and Model-Based Estimates for 6 LHI measures, Anne Arundel County, MD 2001

| Leading Health Indicator Measure |              Prevalence |
| --- | --- |
|  |          ACC               MD State
Survey    Model |
| 22-02.     Moderate physical activity in adults |   25.4%        27.9%            25.2% |
| 27-01a.   Cigarette smoking by adults |   34.7%        21.0%            21.1% |
| 14-29a.   Influenza vaccination in high-risk adults |   72.3%        68.2%            67.3% |
| 14-29b.   Pneumococcal vaccination in high-risk adults |   65.0%        64.2%            62.3% |
| 01-01.     Persons with health insurance |   95.6%        93.6%            89.7% |
| 01-04c.   Specific source of ongoing care |   86.7%        86.1%            85.5% |
|  |  |

## Summary

-  	In general, the model-based estimates fell	between the Maryland State and Anne Arundel 	County survey estimates
-  	Model predicted best for access measures (e.g. 	health insurance, specific source of care)
-  	Model predicted poorly for more complex health 	behaviors (e.g. smoking, physical activity)

## Discussion

-  	Factors to consider:
- 		-	Low AAC survey response rate (25.7%)
- 		-	Small number of predictor variables 				included in models
- 		-	Quality of predictor variables in each model
- 		-	Underlying assumption of synthetic 				estimation

## Contact Information

- Suzanne Proctor, MSPH
- Health Statistician
- National Center for Health Statistics, CDC
- 3311 Toledo Road, Room 6307
- Hyattsville, MD  20782
- (301) 458-4186
- SProctor@cdc.gov