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Factors that Influence – ‘Life Expectancy’:
Multivariate Regression Analysis of US counties
Written by Joo Young Park
PUBP 704-002 : Statistical Method in Policy Analysis
Dec. 8th, 2011
1. Introduction
This paper explores statistically significant factors that influence ‘life
expectancy’. With the help of a multivariate regression analysis of all U.S. counties, this
study shows the factors that influence life expectancy. From the results we can discern
priority of health risk factors for individual health management as well as for health
policy.
Background
‘Life expectancy’ is the expected, in the statistical sense, number of years of life
remaining at a given age, on average, by a particular cohort (Sheffrin, 2003).1 It most
commonly refers to life expectancy at birth, the median number of years that a
population born in a particular year could expect to live. For instance, based on recently
released final data, life expectancy at birth in 2003 was 77.5 years. This tells us that, for
those born in calendar year 2003 in the United States, 50% will die before that age; the
other half will live longer (Shrestha,2006)2
Life expectancy from birth is a frequently utilized and analyzed component of
demographic data for the countries of the world. It represents the average life span of a
newborn and is an indicator of the overall health of a country. In this sense, life
expectancy is one of the factors in measuring the Human Development of each nation,
and also used in describing the physical quality of life of an area.
According to several empirical studies, demographic factors such as race,
gender, and income, direct mortality causes such as cancer, stroke or heart disease,
environmental heath, risk factors for premature death, and access to care service have
been proven as influential factors on Life expectancy. However, the specific level of
1 Steven M. Sheffrin (2003). Economics: Principles in action. Upper Saddle River, New Jersey: Pearson
Prentice Hall. p. 473. 2 Laura B. Shrestha (2006). CRS Report for Congress :Life Expectancy in the United States, Congressional
Research Service, The Library of Congress
2
significances for health-related daily behavioral risk factors and general public supports
on Life expectancy has not been clearly verified, excluding direct mortality causes.
In this sense, to provide specific health risk factors for individual health
management and policy implication, this study will focus on statistical impacts of
general daily behavioral attributes and availability of public health service.
Research Question
In terms of individual behavioral risk factors and availability of public health
supports, what are the influential factors/attributes of ‘Average Life expectancy’ of US
citizens?
2. Literature Review
I & II articles assured that Life Expectancy is a valid indicator of representing
Population health statue. From III to IX, offered considerable independent variables for
the analysis, and X shows recent trend with ‘wellbeing’ trend.
I. Jean Marie Robine, Karen Ritchie, 1991, “Health life expectancy : evaluation of
global indicator of change in population health” BMJ vol.302 :457-460
This text outlines ‘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. It states that future calculations should also take into account the
probability of recovery and thus extend the applicability of the indicator to
populations in poor health rather than focusing on the well population.
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II. Gabriel Gulis, 2000, “Life expectancy as an indicator of environmental health”
European Journal of Epidemiology vol. 16, no.2 :161-165
This article questioned that whether or not life expectancy at birth is related to
the quality of life as expressed by global economic, environmental and nutritional
measures. To get an answer, two models set of independent variables and multivariate
analysis was performed. An attempt to estimate the role of studied variables in overall
life expectancy was done, too. Access to safe drinking water per capita gross domestic
product, literacy, calories available as percentage of needs and per capita public health
expenditures were taken as exposure, and compared with life expectancy at birth. A
linear regression model was used to estimate the role of different exposures on life
expectancy at birth. In the result, the correlation coefficient for the linear model was
0.8823 (R2=0.7784)
III. Anna Peeters, 2003, “Obesity in Adulthood and its consequences for life expectancy :
Life table analysis” Annals of Internal Medicine vol.138 number1 :24-33
Main conclusion of this study is that obesity and overweight in adulthood are
associated with large decreases in life expectancy and increases in early mortality. These
decreases are similar to those seen with smoking. Obesity in adulthood is a powerful
predictor of death at older ages. Thus, the paper contends more efficient prevention
from the prevalence of obesity and its treatment should become high priorities in public
health.
IV. Catherine E. Ross, John Mirowsky, 2002, “Family Relationships, Social Support and
Subjective Life Expectancy” Journal of Health and Social Behavior vol.43 :469-489
This work finds that having adult children and surviving parent’s increases the
length of life on expects, but young children in the home does not, and marriage only
contributes years of life expected for older men.
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According to the interpretation of this article, better current health is associated with
higher subjective life expectancy, but it does not explain the impact of supportive
relationships. Most of the impact of supportive relationships appears to be a direct
result of projected security about the future.
V. 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, vol.10, no.3 :273-278
This study analyzes health expectancy never smokers, ex-smokers and smokers
in Denmark with comparing smoking attributable mortality rate. The results confirmed
that Smoking reduces the expected lifetime in good health and increase the expected
lifetime in poor health
VI. 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, vol.26, no.2 :219-234
This study develops a model within the analytic framework of a Markov-based
multistate life table model to calculate an important indicator of the burden of disease,
the expected years of active life of elderly Indonesians. The result shows that having
some education increases life expectancy but it also expands the expected years with a
major functional problem.
VII. 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 :1629-1641
5
This paper examines healthy life expectancy by gender and education for Whites
and African Americans in the United States at three dates: 1970, 1980 and 1990. There
are large racial and educational differences in healthy life expectancy to each date and
differences by education in healthy life expectancy are even larger than differences in
total life expectancy. Large racial differences exist in healthy life expectancy at lower
levels of education.
Educational differences of healthy life expectancy have been increasing over time
because of widening differentials in both mortality and morbidity. In the last decade, a
compression of morbidity has begun among those of higher educational statues; those
of status are still experiencing expansion of morbidity
VIII. R G Wilkinson, 1992 “Income distribution and life expectancy” BMJ vol. 304 :165-168
This paper suggests that the association between 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.
These results should caution against using the lack of a close relation between national
mortality and gross national product per head to infer that health inequalities within
societies cannot be a reflection of income differentials. Indeed, if health differences
within the developed countries are principally a function of income inequality itself, this
would explain why social class differences in health have not narrowed despite growing
affluence and the fall of absolute poverty.
IX. Robert A. Hahn, Steven Eberhardt, 1995 “Life Expectancy in Four U.S. Racial/Ethnic
Populations:1990” Epidemiology, vol.6, no.4 :350-355
This report used information on population undercounts by race/ethnicity in the
census and on misclassification of race/ethnicity on death certificates to calculate life
expectancy for black, white, American Indian, and Asian men and women in the United
6
States in 1990. The result shows Asian men had life expectancies of 82 years and Asian
women 85.8 years-the highest life expectancies reported for any population in the
world and beyond the limit predicted by some current theories
X. R.J.M. Perenboom, L.M.Van Herten, H.C. Boshuizen, G.A.M. Van Den Bos, 2004
“Trends in life expectancy in wellbeing” Social Indicators Research, vol.65, no.2 :
227-244
According to this paper, contrary to life expectancy in good perceived health and
to disability free life expectancy-which show a decreasing trend- the overall wellbeing of
the population is increasing. It seems that aspects in human life that contribute to
wellbeing or quality of life other than physical health are gaining importance. This
makes life expectancy in wellbeing a less appropriate instrument to monitor changes in
population health, but a useful instrument to measure population quality of life.
3.1 Data
The data utilized in this study comes from CHSI 2009 (Community Health Status
Indicators3) which is a collection of nationally available indicators for counties. CHSI
2009 reported about 205 health indicators for 3,141 current counties in 50 states and
District of Columbia. The data used to construct the Community Health Status Indicators
(CHSI) were obtained from a variety of federal agencies including the Department of
Health and Human Services, Environmental Protection Agency, Census Bureau, and
3 ‘Data Sources, Definitions, and Notes ; Community Health Status Indicators 2009’ : provide health indicator
definitions, sources, and methods used in the Community Health Status Indicators Reports created by the Community
Health Status Indicators (CHSI) Project Working Group. It is not intended to stand alone but to be used as a reference
for the user of the county health profile provided for every U.S. County and is available at
http://www.communityhealth.hhs.gov/homepage.aspx?j=1
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Department of Labor. The CHSI data is reported at the county-level and publicly
available data. (http://www.communityhealth.hhs.gov/homepage.aspx?j=1)
The Community Health Status Indicators (CHSI) Project was initially launched in
2000 and archived in 2004 when the data became outdated. Since then the project was
updated with funding from Robert Wood Johnson Foundation and re-launched by an
expanded partnership that included the Centers for Disease Control and Prevention
(including NCHS and ATSDR), the National Institutes of Health/National Library of
Medicine, the Health Resources Services Administration, the Public Health Foundation,
the Association of State and Territorial Health Officials (ASTHO), National Association of
County and City Health Officials (NACCHO), National Association of Local Boards of
Health (NALBOH), and Johns Hopkins University School of Public Health in 20084. Annual
updates are anticipated with 2009 being the latest.
The CHSI 2009 contains data of direct causes of death such as cancer, heart
disease, homicide, stroke, motor vehicle injuries and others, and represents public
health such as access to and utilization of healthcare services, birth and death measures,
average life expectancy, vulnerable populations, risk-factors for premature deaths,
communicable diseases, and environmental health. In addition, It also covers health
related behavioral factors such as tobacco use, diet, physical activity, alcohol and drug
use, sexual behavior and others substantially contribute to deaths.
As mentioned earlier, the goal of this study is finding implications about the
relationship between ‘average life expectancy’ representing the current health indicator
and general behaviors & public health supports in daily life. In this sense, I excluded the
direct cause factors of death and initially selected 16 variables5.
4 Data Sources, Definitions, and Notes, Community Health Status Indicators 2009
5 Population size, population density, Poverty, Race, unemployed, no exercise, few fruits/vegetables, obesity, smoker,
uninsured, elderly Medicare, Medicaid beneficiaries, primary care physicians, dentist rate, community health center and health professional shortage area.
8
Definitions and Data source of Variables
The definitions of dependent variable and 16 candidates of independent
variables are given as below; Table 1 &2
Table 1. Dependent Variable Used in Analysis6
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
Table 2. 16 Candidates of Independent Variables in Analysis7
6 Data Sources, Definitions, and Notes, Community Health Status Indicators 2009 7 Data Sources, Definitions, and Notes, Community Health Status Indicators 2009
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
9
3.2 Methods
Data Cleaning
Data sets were visually inspected for missing entries and obvious errors. No
missing value was detected, but minor data errors8 in 15 data were observed and
cleaned as missing data. Verification of each race proportion is conducted by checking
8 Not understandable numbers such as decimal errors or minus quantity etc.
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
10
the sum of all races, White, Black, Native American, Indian, Asian and Hispanic, should
be 100%.
Data Recoding
In order to compare relative proportions in each county, data conversions from
the raw number to ratio divided by the population of county is conducted for
Unemployment, Insurance, Elderly Medicare and Medicaid Beneficiaries. Plus, dummy
variable data processing applies for Community health center and Health professional
shortage area, which are composed of nominal data9
Tests for Normality
Normality was evaluated by preparing histograms (See Appendix 2 & 3). Visual
inspection of histograms indicated that each of the distributions were sufficiently
normal to proceed to multivariate analysis.
Identification of Outliers
Outliers were identified by examining histograms (see Appendix 2&3) and box
plots for the dependent variable and each of the independent variables. However, a
decision was made to not eliminate any of the outliers in each variable because there
were so few cases in total10. Moreover, for a number of the outliers, plausible
explanations for the extreme data or unique points of county that relate to the subject
under investigation were apparent; thus, elimination of these outliers could have
inappropriately biased result
Screening for Multicollinearity Prior to Multivariate Regression Analysis
Pearson’s correlation test was performed on the independent variables to
determine whether multicollinearity might be an issue during multivariate regression.
9 Data Sources, Definitions, and Notes, Community Health Status Indicators 2009 10 Less than 10 cases per each independent variable
11
According to the result of Pearson’s correlation coefficients, eliminations were made for
the variables showing more than 0.8 : Population size, Population density, Race portion
of White, Native American and Hispanic, Unemployed, Medicaid beneficiaries, and
Primary care physicians.
In order to double check the potential for multicollinearity , variation inflation
factors (VIFs) were inspected for the initially selected 16 independent variables. (See
Appendix 5) As aligned with Pearson test result, Unemployed, Uninsurance, Elderly
Medicare and Medicaid Beneficiaries appear significant level of correlations.
Multivariate Regression Analysis
Initial model was generated by considering all candidate independent variables
in the order they were entered into SPSS; subsequent several models were constructed
using stepwise deduction of variables, considering regression results as well as VIF
correlation index.
The followings are the specific process and reasons for finalizing the composition
of 11 independent variables for Multivariate regression.
Exclusions of Population size and Population density are decided as their
limited contributions to R2
As the significant level of VIF and Pearson correlation test result
between Unemployment and Uninsurance rate, Uninsurance rate is
chosen to get implications about public health policy
Regarding races, Black and Asian have a clear direction of coefficient in
regression analysis. However, the result of Hispanic is unsecured, and
White & American Indian have no meaningful consequences.
Three variables: Medicaid Beneficiaries, Elderly Medicare and Prime care
physicians show the significant correlation level in VIF. Thus, Elderly
Medicare is selected as its higher coefficient level than others.
Exclusion of Dentist rate is made as its limited coefficient level.
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4. Results
Descriptive Statistics
Table 3: Descriptive Statistics for Dependent Variable
Variable Mean Median Std.Deviation Minimum Maximum Percentile
25 50 75
Average life
expectancy 76.323 76.500 1.9975 66.6 81.3 75.0 76.5 77.7
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
13
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
Regression Results
Final multivariate regression models were generated (See Table 5 below).
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
The regression equation for the final model is:
Average life expectancy = 83.640 -0.081(Poverty)-0.052(Black)+0.050(Asian)
-0.057(no exercise)-0.008(Few Fruit and vegetable)
-0.048(Obesity)-0.109(Smoker)-0.019(Uninsurance)
+0.030(Elderly Medicare)+0.138(Community HC)
-0.059(HPSA)
Positive correlation11 : Community health center > Asian > Elderly
Medicare
Negative correlation : Smoker > Poverty > HPSA > No exercise >
Obesity > Black > Uninsurance > Few fruit and vegetable
11 In order of coefficient
14
Table 6: ANOVA of Final Model
Model Sum of
Squares df
Mean
Square F Sig.
Regression
Residual
Total
5506.510
1943.000
.000
3
45
48
.000
.000
16.829
.000*
* 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
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
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5. Public Policy
A key objective of Public Policy at the local, state and national levels is to
promote Public Health. As discussed in the literature, Average Life Expectancy is a
valuable indicator to monitor Public Health of population and its environments. In this
regard, opportunities to promote Average Life Expectancy deserve the attention of a
wide spectrum of policy makers.
This study generated data which demonstrate that Average Life Expectancy at
the US county level is significantly a function of Poverty, Race (Black, Asian), health
related daily behavioral risk factors (no exercise, Few Fruit and vegetable, Obesity,
Smoker) and political health supports (Uninsurance, Elderly Medicare, Community health
center, HPSA). Therefore, results from this study represent data that policymakers may
want to consider in order to develop or refine county-level strategies to promote Public
health.
County-level policymakers who are interested in developing strategies to
stimulate Public health enhancement may want to consider the following possibilities
which are supported by the specific findings of this analysis:
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
Community_Health_Center_I
nd
.138 .054 .034 2.542 .011 .843 1.186
HPSA_Ind -.059 .074 -.010 -.787 .431 .866 1.155
a. Dependent Variable: ALE
16
Link people to needed personal health services and assure the provision
of health care when otherwise unavailable
Inform and educate people about healthy behaviors
Intensify anti-smoking policies
6. Conclusion
The results of this US county level analysis show some similarities to the results
obtained in previous studies. The final model of this multivariate regression analysis
indicates that Poverty, Black, Asian, no exercise, Few Fruit and vegetable, Obesity,
Smoker, Uninsurance, Elderly Medicare, Community health center, HPSA are significant
inputs to Average Life Expectancy at the US county level. In terms of statistical
coefficient, Community health center, Smoking, and Poverty show relatively higher
influences.
7. Technical appendix
Appendix1. Original Source and Reference of Independent Variables
Poverty Level
The percentage of individuals living below the poverty level in 2008 is data obtained
from the “Small Area Income Poverty Estimates (SAIPE),” U.S. Bureau of the Census and
can be obtained at
http://www.census.gov/did/www/saipe/data/statecounty/data/index.html. Poverty
percent for the composite county of Hoonah-Angoon-Skagway is the weighted (total
census 2008 population) poverty percentage (census poverty percent 2008) for the
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constituent counties (02-105 and 02-230)
Population by Race/Ethnicity
Race- and ethnicity-specific population sizes are from “Annual estimates of the resident
population by age, sex, race, and Hispanic origin for counties: April 1, 2000 to July 1,
2008.” These data are mid-year estimates of the resident population of 2008, and
reflect standard race and ethnicity categories in use by the U.S. Bureau of the Census,
and can be obtained at http://www.census.gov/popest/counties/asrh/CC-EST2008-
alldata.html. Note, the percentages of white, black, Asian American/Pacific Islander, and
American Indian do not total to 100% due to the multiple race category. The percent
Hispanic is non-additive with the race categories. The reader is advised that populations
cross-classified by race and ethnicity (e.g., non-Hispanic white; non-Hispanic black, etc.)
are available at http://wonder.cdc.gov/Bridged-Race-v2008.HTML.
No Exercise
The percentage of adults reporting of no participation in any leisure-time physical
activities or exercises in the past month.
Few Fruits/Vegetables
The percentage of adults reporting an average fruit and vegetable consumption of less
than 5 servings per day.
Obesity
The calculated percentage of adults at risk for health problems related to being
overweight, based on body mass index (BMI). A BMI of 30.0 or greater is considered
obese. To calculate BMI, multiply weight in pounds by 703 and divide the result by
height (in inches) squared.
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Smoker
The percentage of adults who responded “yes” to the question, “Do you smoke
cigarettes now?”
Uninsured Individuals
The estimated number of uninsured individuals under age 65 in the county in 2006 is
from the U.S. Census Bureau, Small Area Health Insurance Estimates Program (SAHIE).
The SAHIE program models county-level health insurance coverage by combining survey
data with population estimates and administrative records. Data and information on
survey methodology and confidence intervals are found at Area Resource File, Health
Resources and Services Administration, 2008.
Community Health Centers
These centers are a source of care for low-income and uninsured individuals and
families and receive a portion of their funding through grants from HRSA. The data is
current as of September 30, 2009. Source: HRSA. Geospatial Data Warehouse,
http://datawarehouse.hrsa.gov/.
Health Professional Shortage Area
These are counties that have been designated as single-county, primary medical care,
health professional shortage areas, as determined by the Secretary of Health and
Human Services, current as of September 30, 2009. Source: HRSA. Geospatial Data
Warehouse, http://datawarehouse.hrsa.gov/.
19
Appendix 2: Histogram of Dependent Variable
20
Appendix 3: Histogram of Independent Variables
21
22
23
Appendix 4: Pearson Test Result of Finally Utilized Dependent Variables
Correlations
Povert
y Black Asian
No_
Exercise
Few_
Fruit_Veg Obesity Smoker
Uninsurance
%
Elderly
Medicar
e%
Dentist
Rate
Community
Health_
Center
HPSA
_Ind
Poverty 1 .462 -.177 .556 .312 .427 .332 .216 -.058 -.293 -.188 -.226
Black .462 1 .006 .263 .099 .285 .033 .038 -.254 -.041 -.219 -.099
Asian -.177 .006 1 -.250 -.269 -.248 -.202 -.025 -.286 .346 -.158 .135
No_Exercise .556 .263 -.250 1 .404 .572 .526 .176 .139 -.388 .014 -.204
Few_
Fruit_Veg .312 .099 -.269 .404 1 .377 .266 .067 .076 -.386 .179 -.265
Obesity .427 .285 -.248 .572 .377 1 .414 -.058 .047 -.336 -.035 -.126
Smoker .332 .033 -.202 .526 .266 .414 1 -.062 -.082 -.236 -.041 -.044
Uninsurance% .216 .038 -.025 .176 .067 -.058 -.062 1 -.040 -.209 -.022 -.270
Elderly_
Medicare% -.058 -.254 -.286 .139 .076 .047 -.082 -.040 1 -.135 .242 -.076
Community_H
ealth_
Center_Ind
-.188 -.219 -.158 .014 .179 -.035 -.041 -.022 .242 -.120 1 -.038
HPSA_Ind -.226 -.099 .135 -.204 -.265 -.126 -.044 -.270 -.076 .330 -.038 1
24
Appendix 5: VIF Test Result of All Candidate Dependent Variables
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 83.168 .508 163.737 .000
Poverty -.090 .006 -.256 -15.026 .000 .504 1.986
Black -.054 .002 -.354 -24.499 .000 .698 1.433
Asian .030 .010 .045 3.105 .002 .700 s1.428
Hispanic .015 .003 .078 5.568 .000 .738 1.355
Unemployed -2.659E-5 .000 -.171 -2.293 .022 .026 38.181
No_Exercise -.059 .006 -.189 -10.249 .000 .428 2.339
Few_Fruit_Veg -.007 .006 -.017 -1.150 .250 .705 1.419
Obesity -.034 .007 -.077 -4.692 .000 .539 1.856
Smoker -.103 .006 -.275 -17.848 .000 .613 1.631
Uninsured -5.194E-7 .000 -.020 -.526 .599 .098 10.178
Elderly_Medicare 7.981E-6 .000 .162 3.270 .001 .059 16.881
Medicaid_Beneficiaries 6.261E-7 .000 .036 .890 .374 .087 11.443
Prim_Care_Phys_Rate .000 .001 .006 .320 .749 .443 2.258
Dentist_Rate .000 .001 .006 .311 .756 .438 2.282
Community_Health_Center_Ind .252 .056 .061 4.517 .000 .791 1.264
HPSA_Ind -.056 .075 -.010 -.751 .453 .848 1.179
25
a. Dependent Variable: Average Life Expectancy