Patterns of Healthy Behavior and Diabetes Medical Outcome · 2015-08-07 · BEHAVIOR AND DIABETES MEDICAL OUTCOME Isabel Corcos, PhD, MPH 1, Leslie Ray, MPH, MPPA, MA 1,2 ... Buy

PATTERNS OF HEALTHY BEHAVIOR AND DIABETES

MEDICAL OUTCOMEIsabel Corcos, PhD, MPH 1, Leslie Ray, MPH, MPPA, MA 1,2, Ryan Smith, MPH2, Barbara M.

Stepanski, MPH, Joshua Smith, PhD, MPH 1, Sanaa Abedin, MPH 2, Amelia Kenner-Brininger, MPH, CPH 1 , Maria Pena, MPH2, Kimberley De Vera, BS1.

1County of San Diego Health and Human Services Agency, Public Health Services, Emergency Medical Services

2County of San Diego Health and Human Services Agency, Public Health Services, Community Health Statistics Unit

5th largest county in the U.S. (4,200 sq. mi.)18 cities, 3.1 million residentsMajor tourist destination, 5 climate zones!

SAN DIEGO COUNTY, CALIFORNIA

41 communities (aggregated census tracts)

• Reflect diverse population, lifestyles, urbanicity across county

• Allow consistent analysis of demographic & health data over time

• Excluding military population which is part of a different medical system

Building Better Health

Living Safely Thriving

THE VISION

3

TODAY’S PRESENTATION FOCUS

Continues work presented at 2014 ESRI Health GIS conference

Showed that health-related data from a large consumer marketing survey correlated plausibly with diabetes medical outcomes

WHAT WE KNOW ABOUT DIABETESIN SAN DIEGO COUNTY

General spatial trends: • Lower rates in north coastal, north central & north inland areas • Higher rates in the southern, eastern areas• In some communities, rates often too statistically unstable to

calculate

LOOKED TO ALTERNATIVE SOURCES FOR HEALTH-RELATED DATA

Hospitalization

Death

ED Discharge

*Sources: Patient Hospitalization Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch. SANDAG, Current Population Estimates, 2012. Emergency Department Discharge Database (CA OSHPD), County of San Diego, Health & Human Services Agency, Public Health Services, Emergency Medical Services. SANDAG, Current Population Estimates, 2012. Death Statistical Master Files (CDPH), County of San Diego, Health & Human Services Agency, Public Health Services, Epidemiology & Immunization Services Branch. SANDAG, Current Population Estimates, 2012.

135.0 143.4

20.7

0

50

100

150

200

Hospitalization Emergency Department(ED) Discharge

Death

San Diego CountyMedical Outcome Rates

per 100,000 population, 2012

Market Potential (2013) data purchased from ESRI • Derived from large, national consumer spending/use survey• Estimates the likely demand for goods & services• Used by business to understand & target consumers

USE OF MARKET POTENTIAL DATA TO PROFILE COMMUNITIES

• Critical factors to course of most chronic diseases

• Available at census tract level

Many questions relate to diet,

exercise, smoking, medication, medical

services use

LIMITATIONS

Based on survey, projected behaviors of adults

Subject to bias/error on part of those surveyed

Variables collected for business purposes, targeting consumers, not for

assessing population health

Relationship with medical outcome is correlative

HOWEVER, WE CAN USE THIS INFORMATION TO

UNDERSTAND WHO’S DOING WHAT AND WHERE,

AND WHO WILL PROBABLY DO IT AGAIN

TODAY’S PRESENTATION FOCUS

Data related to prevalence of diabetes

Spatial patterns of health behaviors

Community health profiles beyond medical outcome rates

VARIABLES CORRELATED WITH DIABETES MEDICAL OUTCOME

Category Variable County %Exercise frequency Spend 6+ hours exercising per week 23.8%

Spend 3-5 hours exercising per week 23.5%Spend 1-2 hours exercising per week 19.5%Spend no time exercising during a typical week 32.6%

Exercise routine Usually follow a regular exercise routine 26.6%Frequently follow a regular exercise routine 33.9%Occasionally follow a regular exercise routine 26.6%Rarely follow a regular exercise routine 12.3%

Exercise location Exercise at club 2+ times per week 16.1%Exercise at other facility (not club) 2+ times/wk 8.1%Exercise at home 2+ times per week 27.9%

Home equipment Own elliptical 3.9%Own stationary bicycle 5.1%Own treadmill 9.1%Own weight lifting equipment 12.6%

Category Variable County %Smoking Smoked cigarettes in last 12 months 16.5%

Smoked 9+ packs of cigarettes in last 7 days 2.8%Method used to stop smoking/12 mo: Cold Turkey 2.7%

Category Variable County %Doctor Visits Visited doctor in last 12 months 73.3%

1-2 times 23.3%3-5 times 20.8%6+ times 29.3%

Health Professional General/Family 38.4%Dentist 35.8%Eye 19.2%Dermatologist 8.3%Internist 7.2%Chiropractor 6.3%Cardiologist 6.1%Physical Therapist 4.6%Ear/Nose/Throat 4.6%Gastroenterologist 3.9%Nurse Practitioner 3.8%Urologist 3.6%Podiatrist 3.0%Allergist 2.1%

PERCENT OF ADULTS IN SAN DIEGO COUNTY WHO BUY/USE

GOODS & SERVICES (OR “DO” BEHAVIOR)

GREEN: SIGNIFICANT NEGATIVE CORRELATION WITH MEDICAL

OUTCOME

RED: SIGNIFICANT POSITIVE CORRELATION WITH MEDICAL OUTCOME

Category Variable County %Food Choices Went to fast food/drive-in restaurant 9+ times/mo 39.7%

Used white bread in last 6 months 38.1%Drank cola (regular) in last 6 months 44.7%Used artificial sweetener in last 6 months 25.9%Try to eat healthy w/nutrition focus 36.6%Rarely check food ingredients before buying 13.3%Frequently check food ingredients before buying 38.5%

Diet Intent Presently controlling diet 35.6%Diet control for blood sugar level 6.6%Diet control for cholesterol level 8.8%Diet control to maintain weight 11.2%Diet control for physical fitness 11.1%Diet control for weight loss 13.3%Used exercise program for diet method 8.4%

Product labeling Buy foods specifically labeled as fat-free 13.0%Buy foods specifically labeled as high protein 6.1%Buy foods specifically labeled as low-calorie 10.6%Buy foods specifically labeled as low-carb 6.0%Buy foods specifically labeled as low-cholesterol 6.8%Buy foods specifically labeled as low-fat 12.3%Buy foods specifically labeled as sugar-free 10.1%

DATA RELATED TO DIABETES PREVALENCE

• Diet control for blood sugar level• Prescription drug use for diabetes

(insulin dependent)• Prescription drug use for diabetes

(non-insulin dependent)

Indicators of diagnosed disease • Positively correlated with diabetes

medical outcomes• Similar to State survey: 7.8% of

San Diego County adults ever diagnosed with diabetes (California Health Interview Survey, 2011)

6.6%

5.1%

9.0%

1.7%

0.4%

2.4%

3.4%

2.6%

4.3%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

County % Community Min (%) Community Max (%)

Diet control for blood sugar level

Used prescription drug for diabetes(insulin dependent)Used prescription drug for diabetes(non-insulin dependent)

Data reflect diagnosed diabetes, and are particularly helpful for communities where rates for medical encounter cannot be calculated

Finds where high values & low values cluster

• Red: hot spot, high values group

• Blue: cold spot, low values group

Intensity of color reflects degree of confidence in results

SPATIAL PATTERNS: HOT SPOT ANALYSIS BY CENSUS TRACT

Hospitalization rates by community shown for reference

SPATIAL PATTERNS: HOT SPOT ANALYSIS OF PREVALENCE-RELATED VARIABLES

Top to bottom:

• Used prescription for insulin dependent diabetes

• Used prescription for non-insulin dependent diabetes

• Diet control for blood sugar

Significant clustering

Pattern consistent across 3 variables

Hot/cold spots correlated with higher/lower rates of all medical outcomes

County mean proportion (%), & the range of values across communities

Diet, exercise & smoking are major players in chronic diseaseCorrelated with diabetes medical outcomesSpatial patterns helpful in understanding these key behaviors

County Minimum (%) Maximum (%)

Try to eat healthy w/nutrition focus 36.6% 29.3% 43.1%

Frequently check food ingredients before buying 38.5% 35.3% 40.8%

Spend 3-5 hours exercising per week 23.5% 18.9% 28.1%

Rarely follow a regular exercise routine 12.3% 8.6% 16.0%

Went to fast food/drive-in restaurant 9+ times/mo 39.7% 28.0% 46.6%

Smoked 9+ packs of cigarettes in last 7 days 2.8% 1.8% 5.4%

BehaviorPercentage of Adults

SPATIAL PATTERNS: HOT SPOT ANALYSIS OF 6 HEALTH BEHAVIOR VARIABLES

SPATIAL PATTERNS: HOT SPOT ANALYSIS BY CENSUS TRACT

Try to eat healthy w/ a nutrition focus

Frequently check ingredients before buying

Spend 3-5h exercisingper week

Went to fast food/drive-in9+ times/mo

Rarely follow regular exercise routine

Smoked 9+ packs of cigarettes last 7 days

Spatial patterns correlate generally with diabetes hospitalization

• Hot spots for high percentage of healthy choices in coastal central and inland valleys

• Cold spots in the south.

• Unhealthy choices show the opposite patterning

Similar to Hot Spot AnalysisIdentifies clusters of, high (HH) low values (LL)

Outliers – areas that differ from others within a specified distance:

HL- high values near low (orange);

LH- low values near high (white)

SPATIAL PATTERNS: CLUSTER & OUTLIER ANALYSIS BY CENSUS TRACT

SPATIAL PATTERNS: CLUSTER & OUTLIER ANALYSIS

Clustering similar to hot spot analysis

Outliers within communities

Many tracts are outliers for multiple variables

Try to eat healthy w/ a nutrition focus

Frequently check ingredients before buying

Spend 3-5h exercisingper week

Went to fast food/drive-in9+ times/mo

Rarely follow regular exercise routine

Smoked 9+ packs of cigarettes last 7 days

Understanding neighborhood differences can assist in supporting healthy choices, or find

areas for targeted outreach

PROFILING HEALTHY COMMUNITIES

Objective:• Identify communities where

more people make very healthy choices

• Identify communities where fewer healthy choices are made

• Identify which behaviors need support or need change
















Ranked communities in deciles from healthiest to least healthy for each variable















Composite profile for each category

Frequently check

ingredients

Buy low-calorie foods Diet for

physicalfitness

Diet Exercise

Medical services Smoking

Healthy Community Profile


Most to Least Healthy Overall profile, by health behavior category, and by medical outcome

Healthy behaviors cluster

Communities with lower medical outcome rates also have more health-oriented adults (correlative)

Outliers are apparent

Jamul (healthy, but more disease

University (less healthy, low disease)

CommunityHealthy

Community Profile

Diet Exercise Medical Services Smoking Death Hospitalization ED Discharge

Poway 1.8 2.5 1.7 1.7 1.3 4.0 1.0 4.0 San Dieguito 1.9 2.3 2.3 2.2 1.0 2.0 3.0 2.0 North San Diego 2.4 2.1 2.9 2.3 3.0 3.0 3.0 Jamul 2.5 3.7 2.0 1.8 2.7 9.0 8.0 Carlsbad 2.6 2.9 2.4 2.8 2.3 2.0 2.0 2.0 Coastal 2.9 2.9 3.5 3.4 1.7 1.0 1.0 2.0 Valley Center 3.0 4.1 2.7 2.8 2.7 3.0 4.0 Del Mar-Mira Mesa 3.1 3.2 2.7 4.3 2.3 1.0 2.0 2.0 Sweetwater 3.2 3.6 2.4 4.3 2.3 1.0 2.0 1.0 Alpine 3.5 4.7 3.6 2.7 3.0 9.0 5.0 3.0 Elliott-Navajo 3.8 3.7 4.0 3.4 4.0 5.0 4.0 5.0 Harbison Crest 3.9 4.1 3.3 2.8 5.3 Peninsula 4.7 4.3 5.6 6.3 2.7 4.0 3.0 Coronado 4.7 4.6 3.2 5.1 6.0 2.0 1.0 Fallbrook 4.8 5.2 4.1 4.6 5.3 4.0 6.0 6.0 Ramona 4.9 5.4 3.9 5.3 5.0 3.0 7.0 5.0 Santee 5.1 5.5 5.0 3.9 6.0 6.0 8.0 6.0 University 5.2 5.3 6.9 7.5 1.3 2.0 1.0 1.0 Kearny Mesa 5.3 4.6 5.8 5.5 5.3 8.0 5.0 7.0 Palomar-Julian 5.5 5.0 6.0 2.9 8.3 5.0 Spring Valley 5.7 6.1 6.3 5.3 5.0 3.0 8.0 8.0 La Mesa 5.7 5.0 6.3 4.9 6.7 9.0 8.0 8.0 San Marcos 5.8 5.8 5.6 5.6 6.0 7.0 4.0 4.0 Oceanside 6.4 6.5 6.2 6.3 6.7 8.0 6.0 6.0 Central San Diego 6.5 5.7 7.3 7.9 5.0 6.0 9.0 9.0 Anza-Borrego Springs 6.5 6.3 6.9 3.8 9.0 7.0 5.0 Escondido 6.7 7.0 6.8 7.4 5.7 7.0 6.0 7.0 Pauma 6.8 6.2 5.6 7.9 7.3 3.0 3.0 Laguna-Pine Valley 6.8 6.5 6.4 5.5 9.0 Lakeside 7.1 6.9 6.7 5.9 8.7 6.0 4.0 5.0 Vista 7.2 7.4 8.0 7.6 5.7 5.0 7.0 8.0 Lemon Grove 7.4 7.6 8.0 7.4 6.7 4.0 9.0 9.0 El Cajon 7.8 7.3 8.0 7.1 8.7 8.0 7.0 6.0 Mountain Empire 7.8 7.3 7.6 6.4 10.0 10.0 6.0 7.0 South Bay 8.2 8.2 9.3 9.8 5.7 9.0 10.0 9.0 Southeastern San Diego 8.2 8.5 9.1 8.9 6.3 7.0 10.0 10.0

Chula Vista 8.5 8.6 9.3 8.9 7.3 10.0 9.0 10.0 Mid-City 8.7 8.1 8.7 9.0 9.0 5.0 8.0 9.0 National City 8.7 8.7 9.7 9.8 6.7 10.0 10.0 10.0

Composite Healthy Behaviors Ranks Medical Outcome

WHAT THIS ADDS TO OUR GOAL OF BUILDING BETTER HEALTH

Census tract-level data for detailed analysis of community

Estimates of diagnosed diabetes prevalence

Verification that correlations have a significant spatial component

Evaluate community health in areas where medical outcome rates are not

calculable

WHAT THIS ADDS TO OUR GOAL OF BUILDING BETTER HEALTH

Ability to monitor trends & forecast challenges to

population health

Enriched evidence base for data-driven decision-making

Insight into collective impact of key health behaviors

For more information please contact

Dr. Isabel CorcosCounty of San Diego, Health & Human Services Agency

Public Health Services, Emergency Medical [email protected]

Patterns of Healthy Behavior and Diabetes Medical Outcome · 2015-08-07 · BEHAVIOR AND DIABETES MEDICAL OUTCOME Isabel Corcos, PhD, MPH 1, Leslie Ray, MPH, MPPA, MA 1,2 ... Buy

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