Factors that Influence – ‘Life Expectancy’:

Multivariate Regression Analysis of US counties

Joo young Park

Contents

Introduction

Literature Review

Data & Methods

Results

Public Policy

Life expectancy is the expected number of years of life

remaining at a given age, in the statistical sense.

(Sheffrin, 2003)

1.0 Introduction

Steven M. Sheffrin (2003). Economics: Principles in action. Upper Saddle River,

New Jersey: Pearson Prentice Hall. p. 473.

Potential factors that drive Life Expectancy

Demographic factors :race, gender, income

Direct mortality causes: cancer, stroke, heart disease

Environmental heath: air quality, toxic chemical

Risk factors for premature death : smoking, obesity

Access to care service

1.0 Introduction

Research Question:

In terms of individual behavioral risk factors &

availability of public health supports,

what are the influential factors

of ‘Average Life expectancy’ of US citizens?

1.0 Introduction

2.0 Literature Review

Jean Marie Robine, Karen Ritchie, 1991, “Health life expectancy :

evaluation of global indicator of change in population health”

‘Healthy life expectancy’ is a valuable index for the appreciation of changes in both the physical and the mental health states of the general population, for allocating resources, and for measuring the success of political programs

Gabriel Gulis, 2000, “Life expectancy as an indicator of environmental health” European Journal of Epidemiology

Life expectancy at birth is related to the quality of life as expressed by global economic, environmental and nutritional measures.

2.0 Literature Review

Anna Peeters, 2003, “Obesity in Adulthood and its consequences for life

expectancy : Life table analysis” Annals of Internal Medicine

Obesity and overweight in adulthood are associated with large decreases in life expectancy and increases in early mortality

Henrik Bronnum-Hansen, Knud Juel, 2001 “Abstention from smoking

extends life and compresses morbidity: a population based study of health expectancy among smokers and never smokers in Denmark” Tobacco Control

Smoking reduces the expected lifetime in good health and increase the expected lifetime in poor health

2.0 Literature Review

Mira M.Hidajat, Mark D.Hayward, Yasuhiko Saito, 2007 “Indonesia’s social

capacity for population health: the educational gap in active life expectancy” Population Research and Policy Review

Education increases life expectancy but it also expands the expected years with a major functional problem.

Eileen M. Crimmins, Yasuhiko Saito, 2001 “Trends in healthy life expectancy in the United States, 1970-1990: gender, racial, and educational differences” Social Science & Medicine 52

Large racial and educational differences in healthy life expectancy

2.0 Literature Review

R G Wilkinson, 1992 “Income distribution and life expectancy” BMJ vol. 304 :165-168

Health and income distribution is a result of factors to do with relative rather than absolute income. Increasingly social scientists have emphasized the importance of relative poverty.

Robert A. Hahn, Steven Eberhardt, 1995 “Life Expectancy in Four U.S. Racial/Ethnic Populations:1990” Epidemiology

Race/ethnicity on death certificates to calculate life expectancy for Black, White, American Indian, Hispanic and Asian men and women in the United States in 1990. Asian men had life expectancies of 82 years and Asian women 85.8 years-the highest life expectancies

3.1 Data

Dependent

Variable Definition Data source Year

Life Expectancy

The average number of years that a baby born in a particular

year is expected to live if current age-specific mortality trends

continue to apply

The Community Health Status Indicators Report by Department of Health

and Human Service

2008

Data set : CHSI 2009 (Community Health Status Indicators) 205 health indicators for 3,141 current counties in 50 states and District of Columbia

3.1 Data (Continued)

Independent

Variable

Expected

sign Definition Data source Year

Population Size unclear Annual estimates of the resident population US Census Bureau 2008

Population

Density negative Population density (people per square mile) US Census Bureau 2008

Poverty negative individuals living below poverty level % US Census Bureau 2008

Population

Race/Ethnicity unclear

Race and ethnicity-specific population size %

; White/Black/

American Indian/Asian/Hispanic

US Census Bureau 2008

Unemployed negative Unemployed % US Bureau of Labor Statistics 2008

No Exercise negative

% of adults reporting of no participation

in any leisure-time physical activity or exercises in

the past month

Centers for Disease Control

and Prevention 2006

Few fruits/

vegetables negative

% of adults reporting an average

fruits/vegetables

consumption of less than 5 servings per day

Centers for Disease Control

and Prevention 2006

Obesity negative Calculated % of adults of overweight,

based on body mass index (BMI)

Centers for Disease Control

and Prevention 2006

Smoker negative % of adult smoker Centers for Disease Control

and Prevention 2006

Uninsured negative Estimated % of uninsured individuals

under age 65 US Census Bureau 2006

Medicaid

Beneficiaries positive Medicaid beneficiaries

Centers for Medicare and Medicaid

Services 2008

Primary care

physicians positive Primary care physicians per 100,000 pop % HRSA 2008

Elderly

Medicare positive

% of Medicare beneficiaries

for elderly (age 65+)

Centers for Medicare

and Medicaid Services 2008

Dentist Rate positive dentists % per 100,000 pop HRSA 2008

Community

Health Center positive

Indicator for any Community/Migrant Health

Centers located in the county HRSA 2009

HPSA positive Indicator for single county designated

Health Professional Shortage Area HRSA 2009

Data Cleaning

Data Recoding : % conversion

Unemployment, Insurance, Elderly Medicare and Medicaid Beneficiaries

Test for Normality: histogram

Identification of Outliers

Screening and Tests for Multicollinearity

3.2 Methods

4.2 Results: Regression Models

Multivariate Regression Analysis

• Exclusions of Population size and Population density

• VIF and Pearson correlation test :

1) Unemployment vs.Uninsurance

2) Medicaid Beneficiaries, Elderly Medicare, Primary care

physicians

• Races, Black & Asian : clear direction, But Hispanic -unsecured,

White & American Indian no direction

• Exclusion of Dentist rate : limited coefficient level

4.2 Results: Regression Models

Table 7: Coefficients* and Collinearity Statistics of Final Model

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

(Constant) 83.640 .534 156.654 .000

Poverty -.081 .006 -.229 -13.530 .000 .512 1.952

Black -.052 .002 -.345 -23.612 .000 .687 1.455

Asian .050 .009 .076 5.418 .000 .744 1.344

No_Exercise -.057 .006 -.183 -9.658 .000 .407 2.459

Few_Fruit_Veg -.008 .006 -.019 -1.337 .181 .730 1.369

Obesity -.048 .008 -.107 -6.363 .000 .518 1.931

Smoker -.109 .006 -.290 -18.763 .000 .613 1.632

Uninsurance% -.019 .006 -.041 -2.947 .003 .767 1.304

Elderly_Medicare% .030 .007 .057 4.113 .000 .766 1.306

Community_Health_Center_I

nd

.138 .054 .034 2.542 .011 .843 1.186

HPSA_Ind -.059 .074 -.010 -.787 .431 .866 1.155

4.2 Results: Regression Models

Table 5: Summary of Final Model

Model R R2 Adjusted R2 Std. Error of

the Estimate

Regression 2 0.860* 0.739 0.737 1.0451

* a. Predictors: (Constant), Poverty, Uninsurance%, Black, Asian, Few_Fruit_Veg, Obesity, No_Exercise, Smoker, Elderly_Medicare%, Community_Health_Center_Ind, HPSA_Ind, b. Dependent Variable: ALE

Average life expectancy

= 83.640 +0.138(Community HC) +0.050(Asian)

+0.030(Elderly Medicare) -0.109(Smoker)

-0.081(Poverty) -0.059(HPSA)-0.057(no exercise)

-0.052(Black) -0.048(Obesity)-0.019(Uninsurance)

-0.008(Few Fruit and vegetable)

4.2 Results: Regression Models

• Mobilize and enhance Community Health Centers which are

for low income and uninsurance care

• Develop policies and plans that support HPSA

• Assures the quality and accessibility of health services,

especially in HPSA

• Inform and educate people about healthy behaviors

• Intensify anti-smoking policies

5.0 Public Policy

Any Questions?

The End

4.1 Results: Descriptive Statistics Table 4: Descriptive Statistics for Independent Variables

Variable Mean Median Std.Deviation Minimum Maximum Percentile

25 50 75

Poverty 15.24 14.30 6.06 3.10 54.40 10.90 14.30 18.30

Black 9.12 2.30 14.39 0.00 86.00 0.60 2.30 10.60

Asian 1.18 0.50 2.77 0.00 55.60 0.30 0.50 1.00

No Exercise 26.51 26.00 6.70 8.30 52.40 21.90 26.00 30.80

Few fruits/

vegetables 78.92 79.00 5.16 63.10 96.40 75.50 79.00 82.40

Obesity 24.15 24.30 4.90 4.20 42.60 21.10 24.30 27.20

Smoker 23.11 23.00 5.73 3.60 46.20 19.40 23.00 26.70

Uninsured 15.10 14.41 5.08 0.00 41.91 11.33 14.41 18.02

Elderly

Medicare 14.76 14.30 4.26 0.00 38.10 11.98 14.30 17.16

Community

Health

Center

0.51 1.00 0.50 0.00 1.00 0.00 1.00 1.00

HPSA 0.75 1.00 0.43 0.00 1.00 1.00 1.00 1.00