physical activity and health risks

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Physical Activity and Health Risks

Meghan Nairn Research completed in partial fulfillment of the requirements

For the degree of Master of Science in Demography

Center for Demography & Population Health Florida State University

August 2011

COMMITTEE APPROVAL

________________________________________ Professor Isaac W. Eberstein, Chair

________________________________________ Professor Elwood D. Carlson

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ABSTRACT

This paper will examine the relationship between level of physical activity

and health related behaviors. The purpose is to examine if less active or more active

people are more or less likely to see doctors for an annual medical check –up, if

more active people are less likely to smoke, and also if more active people are less

likely to drink. Previous literature will be used as a background for this study and to

explain some connections that have already been made between these variables.

Demographics including age, sex, race, income and education will be controlled. Self-‐

reported health will also be used to control for health status. The data used were

taken from BRFSS 2007 telephone survey, which asks health related questions to

those 18 and older. Logistic regression was used to find that as activity levels

increase , one is less likely to see a doctor within the year, less likely to smoke, and

more likely to drink. These relationships change directions when demographics are

controlled for and change within each model when self-‐reported health, age, sex,

income, and education are controlled for. It was concluded that the least active

group has higher odds than the highest active group to have seen a doctor for a

medical check-‐up within the past year, those with moderate activity level have the

highest odds of smoking, and those with the highest activity level have the highest

odds of being a moderate or heavy drinker compared to being a non-‐drinker.

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INTRODUCTION

The purpose of this study is to examine the relationship between physical

activity and health behavior. The objective is to explore the association between a

person’s level of physical activity and risky health behaviors such as smoking and

drinking. It will also test if activity is associated with annual doctor visits by asking if

a person has seen a physician within the past year. Although the direction of the

relationship is ambiguous to predict, the expected result is that there will be an

association between activity level and doctor visits and those who are more active

will smoke and drink less than those who are less active.

PREVIOUS LITERATURE

Many studies have noted the positive health effects of physical activity. It has

been linked to reduced premature mortality in numerous ways. Physical activity can

reduce the risks of coronary heart disease, hypertension, colorectal cancer, obesity,

and osteoporosis ( Blair,1996). Physical activity can help reduce the risk of

overweight or obesity, which leads to decline in physical health. One study showed

that even if physical activity did not lead to extreme weight loss, for those

individuals who are obese, physical activity may still provide health benefits

(Penedo & Dahn, 2005). Physical activity can also have positive effects on mental

health. It can improve confidence, well-‐being, anxiety reduction and intellectual

functioning. (U.S. Department of Health & Human Services, 1996).

Some authors have concluded that a person’s involvement in physical activity

can be influenced by various factors. Self efficacy has been shown to be a correlate

of physical activity ( Trost et al. 2002). Demographics have also been noted. Men

have been observed to have higher levels of physical activity than women, Whites

are more physically active than Blacks and Hispanics, education and income are

both positively associated with physical activity, and participation in physical

activity declines with age (Casperan et al. 1992, Casperan et al. 1995). Some studies

have also reported that physical activity can have an impact on a person’s mental

health in addition to their physical health. Physical activity can improve confidence,

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well-‐being, anxiety reduction and intellectual functioning (Hughes 1984). McAuley

1994 has shown that there are correlations between exercise and self-‐steem, self-‐

efficacy, psychological well-‐being and cognitive function as well as anxiety, stress

and depression.

There are different ways to look at the relationship between physical activity

and health behavior. Some examine physical activity as having an impact on health,

while others examine a person’s health as having an impact on their level of activity.

Warburton et al. 2006 reviewed the health benefits of physical activity from the

view point that physical activity is what impacts health. They concluded that

physical activity does contribute to the prevention of some chronic diseases and

reduces the risk of premature death. Increased activity in both men and women

reduces the relative risk of cardiovascular related death, and increased energy

expenditure was associated with a mortality benefit of 20%. Increased energy

expenditure also decreased the incidence of type 2 diabetes by 6%, and weight loss

through diet and exercise reduced the incidence of high risk individuals by 40%-‐

60% over a 4 year period. Physical activity can also impact certain cancers,

specifically colon and breast. Men and women showed 30%-‐40% reduction in the

relative risk of colon cancer, and physically active women showed 20%-‐30%

reduced risk of breast cancer compared to their inactive counterparts (U.S.

Department of Health and human Services 1996).

The other direction to be considered is that a person’s existing health

condition will affect their ability to engage in physical activity. The Center for

Disease Control reports that many older adults live with , rather than die from,

disabling chronic disease. Chronic disease or physical handicaps may reduce a

person’s mobility which would lead to a sedentary lifestyle. Attempts to change

levels of activity are met by many barriers. One study found that older adults are

less likely than younger adults to engage in leisure-‐time physical activity when they

perceive their neighborhood as unsafe ( Center for Disease Control,1999). Poor

perceived health, pain or fear of pain as a result of a chronic disease may also

influence a sedentary lifestyle compared to a physically active one ( Brawley, 2003).

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Cigarette smoking is noted as the top preventable cause of mortality in the

United States. It is estimated that 8.6 million American suffer from conditions

caused by cigarette smoking such as selected cancers, heart attack, stroke, chronic

bronchitis and emphysema (Center for Disease Control 2002; 2003). Since reports

like these have come out over the years the rates of smoking have started to decline,

however smoking habits are not the same for all demographic groups and have

shown to vary across subgroups of race, ethnicity, age, and social class (Keife 2001,

Peirce 1989,Fiore 1989,Flint 1998).

According to the Centers for Diseases Control, as of 2010 an estimated 46

million adults smoke cigarettes in the United States. Current smokers are defined as

those who have smoked at least 100 cigarettes in their lifetime and currently smoke

cigarettes some days or every day. More men than women fall into this category

with 23.5% of adult males being current smokers and 17.9% of adult females being

smokers. Twenty-‐two percent of whites smoke, closely followed by 21% of blacks

and 14.5% of Hispanics. Those with lower education had higher percentages of

smoking. Of those who have a High School diploma, 49% are smokers, 33.6 % of

adults with 9 to 11 years of education are smokers, 11.1% for adults with an

undergraduate degree and 5.6% of adults who have a graduate college degree. In

terms of life expectancy of smokers, those who quit smoking, on average, live longer

than those who are continuous smokers. Those who have never smoked have the

longest life expectancies, followed by those who quit smoking, with continuous

smokers having the shortest life expectancy ( Nam et al 1994, Rogers and Powell-‐

Grinner 1991).

Close behind smoking among preventable causes of death is alcohol abuse,

which is ranked third in the United States (McGinnis 1993). (Second is high-‐blood

pressure, which has smoking, diet, and exercise as major risk factors[ Danaei et al.,

2009]). Binge drinking relates to alcohol related deaths in multiple ways and is

generally defined as 5 or more alcoholic beverages on 1 occasion (Weshcler 1998,

Weschler 2001). One study focused on the sociodemographic characteristics of

older adults and unhealthy drinking patterns. It was found that women are more

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likely than men to report no drinking and less likely to report unhealthy

consumption, heavier alcohol consumption is more prevalent among Whites than

any other race, and higher levels of education and income are associated with a

higher prevalence of drinking (Merrick 2006).

Previous research has shown that 95% of women regardless of age or race

seek medical care within a year’s time. The most frequently cited reasons for visits

were medical examination followed by progress visits. This is 33% higher than

males’ rates of seeking ambulatory medical care within the past year. The number of

visits increases as age increases. The percentage of Blacks who had an ambulatory

visit within the past year was slightly higher than the percentage of whites. Black

women were three times more likely than white women to be covered by Medicaid,

which covered 9% of visits (Brett 2001).

This research will examine the relationship between level of physical activity

and health related behaviors. The behaviors will be medical exam within the past 12

months, smoking, and being a moderate or heavy drinker versus a non-‐drinker.

Variables are constructed by using definitions set by Centers for Disease Control as

well as other studies which are noted in the Data and Methods section. A logistic

regression is used to determine the direction and significance of these relationships.

DATA and METHODS

The data used in the research are taken from the BRFSS ( Behavioral Risk

Factor Surveillance Survey) for the year 2007. The BRFSS is a random-‐digit-‐dialed

telephone survey of the non-‐institutionalized U.S population aged 18 years and

older. The telephone numbers represent a sample of the population of households

with a telephone. It is a state-‐based health survey administered by the Center for

Disease Control and covers all 50 states, the District of Colombia, Puerto Rico, Guam

and the Virgin Islands. Although the data are available with post stratification

weights, this research will use an unweighted sample. Several questions from the

survey were used to construct variables for physical activity, smoking habits,

drinking habits, and visits to a physician. The data set has 430,912 participants,

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after excluding respondents with incomplete or missing data on physical activity

and other variables in the study, the number of observations in the analytical

sample was reduced to 268, 522. The large number of missing observations is

referenced in the discussion section of this paper.

Physical Activity

Four categories were constructed: none, low, moderate, and high. The first

question asked in the physical activity section is “ In a usual week , do you do

moderate activities for at least 10 minutes at a time, such as brisk walking, bicycling,

vacuuming, gardening, or anything else that causes some increase in breathing or

heart rate?” Those who answered “yes” were then asked how many minutes they

are moderately active for a day, and how many days per week are they moderately

active. If a respondent answered “no” they were then directed to the question “ In a

usual week do you do vigorous activities for at least 10 minutes at a time such as

running, aerobics, heavy yard work or anything else that causes large increases in

breathing or heart rate?” Those who answered “no” to both of these questions are

defined as “None”. Those who answered they engage in vigorous activity were

asked how many days per week do they engage in this activity as well as how much

time they spend doing these activities on those days. The number of minutes and

days were then multiplied. The CDC recommends that a person engage in 30

minutes of activity preferably every day of the week, but at least on most days.

Those who did a total of moderate activity for 120 – 210 minutes per week are

categorized as “Moderate” meaning they meet the recommended level. Those who

answered they engage in moderate activity for less than 120 minutes are

categorized as “Low” and those who engage in moderate activity for over 210

minutes per week are categorized as “High”. Those who answered that they engage

in vigorous activity for 50 – 100 minutes are categorized as “ Moderate”, those who

engage in vigorous activity for less than 50 minutes per week are “ Low” and those

engaging in over 100 minutes are “High”. The construction of these variables is

based on other research which used similar definitions and measures (Brown

2003,Brown 2004,Sherwood 2000).

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Annual Checkup

To determine how often a respondent visits a doctor the question

asked was “ About how long has it been since you last visited a doctor for a routine

checkup? A routine checkup is a general physical exam not an exam for a specific

injury , illness, or condition.” With the options to answer “ Within the past year,

Within the past 2 years, Within the past 5 years, 5 or more years ago.” For the

purpose of this research respondents are grouped as either visiting the doctor

within the past year, or if it has been longer than one year since their last checkup.

Smoking

Respondents were put into two categories for smoking : Smoker or Non-‐

Smoker. Non-‐Smokers are those who have smoked less than 100 cigarettes in their

entire life or those who have smoked more than 100 cigarettes but currently do not

smoke. Those who answered they currently smoke are classified as smokers. Those

who said they have ever smoked more than 100 cigarettes, but are not current

smokers were categorized as non-‐smokers. Other literature has shown that ex-‐

smokers have better quality of life and significant improvement in respiratory

symptoms, effects from long term disease, as well as other health benefits that are

generally associated with non-‐smokers (Rothenberg 1990).

Drinking

Respondents were first asked “During the past 30 days have you had at least

one drink of any alcoholic beverage such as beer, wine, a malt beverage or liquor?”

Those who answered “No” were put into the “Non-‐Drinker” category. Those who

report at least one alcoholic drink within the past 30 days were then asked “During

the past 30 days how many days per week did you have at least one drink of any

alcoholic beverage?” as well as “One drink is equivalent to a 12-‐ounce beer, a 5-‐

ounce glass of wine, or a drink with one shot of liquor. During the past 30-‐days, on

days when you drank, about how many drinks did you drink on average?” To

determine the number of drinks a respondent had in one week the number of days

they said they drink was multiplied by the average number of drinks they had in a

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sitting. For females moderate alcohol consumption is considered to be one drink per

day, and 2 drinks per day for males. Women who drank 1 to 7 drinks per week are

categorized as “ Moderate” and Men who drank 1 to 14 drinks per week are

categorized as “Moderate.” Females drinking more than 7 and Males drinking more

than 14 are categorized as “Heavy.”

Analysis

A logistic regression was used to examine the relationship between annual

checkups and smoking. Ordered logit was used to examine drinking and activity

levels. A chi-‐square was run on activity and demographic variables to examine

associations. Table 1 displays descriptive statistics from the Chi-‐Square. The

number of observations for each activity level by demographic characteristics is

shown. Table 2 displays the odds ratios for activity level and doctor visit within the

past year. The first model does not control for any variables, Model 2 through Model

4 controls for self-‐reported health, age, sex, race, income and education. Table 3

displays odds ratios for activity level and smoking. Once again the first model has no

controls. Model 2 through Model 4 implement control variables. Table 4 displays

odds ratios for activity level and drinking. The models follow the same pattern as

the previous tables.

As demonstrated in the tables, four models were run. The first model does

not control for any other variables and the results shown are just for activity levels

vs. the dependent variable (annual checkup, smoking, and drinking) . Model 2

controls for a person’s self evaluated health and the reference group is ‘Excellent’.

Model 3 controls for self evaluated health, age, sex, and race. The reference groups

are 18-‐34 for age ( the youngest age group), white for race and males for sex. Model

4 controls for all of the previous including Income and Education. The reference

groups are less than $10,000 for income and Less than High School for education.

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RESULTS

Table 1 through Table 3 show percentage distributions of the dependent

variables. Table 1 displays medical exams by smoking status. The percentages of

smokers who have seen a doctor within the past year is lower than the percentage

of non-‐smokers who have a seen a doctor within the past year. Table 2 displays

medical exams by drinking status. Non-‐ drinkers have the highest percentage of

those who have seen a doctor within the past year. The percentage drops for

moderate and more for heavy drinkers. These two tables show that those who do

not drink or smoke display the highest percentages for receiving a medical exam

within the past year.

Table 3 displays smoking status by drinking status. This table shows that

among drinkers, the highest percentage of smokers is found in the ‘heavy drinking’

category. The percentage of smokers in the ‘heavy drinking’ category is double the

percentage of smokers in the ‘none’ and ‘moderate’ drinking categories. This

suggests that smoking and heavy drinking may go hand in hand with each other.

Table 4 displays a cross classification of activity level by all of the dependent

variables in the analysis. The Chi-‐Square tests showed a significant in relationship

with each variable with a p-‐value of <.001. Eleven percent of Males are meeting the

‘moderate’ level of activity while 28% are meeting the requirements for ‘high’

activity levels. Nine percent of Females are meeting the ‘moderate’ level while 17%

are meeting the ‘high’ level. This shows us that Males are more active than Females.

Since the data are unweighted we see a sample that is predominantly female. Thirty

five percent of the sample is male and 65% is female. Whites and ‘Other’ races are

the most active, followed by Hispanics and Blacks. Younger age groups are the most

active, activity declines as age increases. Those with higher income and education

have higher activity levels than those with lower income and education.

Annual Checkup ( Table 5)

Activity is significantly related to having had a medical exam within the past

year ( p-‐value < .0001) when no other variables are controlled. Compared to an

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activity level of ‘None’, all other activity levels are less likely to have seen a doctor

within the past year. As level of activity increases, it is increasingly less likely that

one has seen a doctor within the past year. When controlling for self reported

health, this pattern remains. Model 3 begins to control for demographic variables. In

Model 3 activity level ‘Low’ is no longer significant when compared to

‘None’.’Moderate’ and ‘High’ remain significant, however they change directions.

‘Moderate’ and ‘High’ now become more likely than those with no activity to have

seen a doctor within the past year. When all variables are controlled, only one

activity level remains significant when compared to ‘None’. ‘High’ activity levels are

significant at level .03. Those with ‘High’ activity level compared to those of ‘None’

are less likely to have seen a doctor within the past year. Activity levels ‘Low’ and

‘Moderate’ have no significant difference when compared to ‘None’.

Those who reported ‘Poor’ health are the most likely to go to the doctor

within a year as compared to those with ‘Excellent’ health’. As self-‐reported health

goes from ‘Poor’ to ‘Excellent’, the chances of seeing a doctor within a year become

less. Females are more likely to have seen a doctor within the past year than males,

older age groups are more likely to have a seen a doctor within the past year than

younger age groups. All race groups are more likely than Whites to have seen a

doctor within the past year with Blacks being the most likely, followed by Hispanics

and then “Other”. As education rises so does the likelihood of seeing a doctor within

the past year. Incomes of $25,000 or more are significant, as income rises so do the

chances of one seeing a doctor within the past year.

Activity level and doctor visits are significant. The directions between the

reference group and levels of activity change based on which demographics are

being controlled. The result of controlling for all demographic variables is that only

those with a sedentary lifestyle compared to those with the highest level of activity

show significance differences. Those who are sedentary are more likely to have seen

the doctor within the past year as opposed to those who exhibit the highest physical

activity level.

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Smoking (Table 6)

Model 1 shows that when no variables are controlled, activity is significantly

related to being a non-smoker (p-value <.001). Those with an activity level of ‘Low’ or

‘High’ are most likely to be non-smokers; ‘Moderate’ activity is least likely to smoke

compared to ‘None’. When controlling for self-reported health, activity level ‘Low’ is no

longer significant compared to ‘None’. ‘Moderate’ level activity is still significant with a

less likely chance of smoking, and ‘High’ remains significant but changes its effect,

and those within the group become more likely to smoke than the ‘None’ group. Model

3 controls for age, sex, and race. Activity becomes significant at all levels again and

‘Low’, ‘Moderate’ and ‘High’ are all less likely to smoke than ‘None’. When all

variables are held constant the only activity level significant to ‘None’ is

‘Moderate’. ‘Moderate’ is less likely than ‘None’ to be a smoker. ‘Low’ and ‘High’

show non-significant p-values (.1708,.7422) when compared to ‘None’. As self-reported

health becomes worse, the odds of smoking become greater. Females are less likely to

smoke than males, blacks and Hispanics are less likely to smoke than whites, and all age

groups are less likely to smoke than 18-34 year olds. The 35-49 year olds, however, are

not significantly different from 18-34 year olds as being a smoker vs. non-smoker in

Model 3. Moving to Model 4, the p-value becomes significant (<.0001) suggesting that

35-49 year olds are more likely to smoke than 18-34 year olds. Education and income

are both significant with all levels of education being less likely to smoke than ‘Less than

High School’ as well as all levels of income being less likely to smoke than ‘ Less than

$10,000’. As both income and education rise the odds of smoking become less.

Physical activity and smoking are significant when no demographic variables are

controlled . Once age, sex, race, income, and education are controlled ‘Moderate’, it is

the only activity level which remains significant to the reference group of ‘None’. Having

‘Low’ activity or ‘High’ activity is not significant to a sedentary lifestyle when

examining whether a person is a smoker or non-smoker.

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Drinking ( Table 7)

When no variables are controlled, all levels of activity are significant with p-‐

value <.001. An ordered logit shows that as activity levels rise so do the odds of

being a moderate or heavy drinker as opposed to not drinking. When controlling for

self-‐reported health this pattern remains. Activity level and drinking remain

significant across each model. All activity groups have greater odds of being

moderate or heavy drinkers compared to ‘None’. As activity levels rise, so do the

odds of being a drinker.

Self-‐reported health is also significant and as health increases from ‘Poor’ to

‘Excellent’ , the odds of being a moderate or heavy drinker increase. Sex, race, age,

income and education are all significant as well. Females are less likely than males

to be moderate or heavy drinkers, all races are less likely than Whites to be

moderate or heavy drinkers. As both income and education rise, the ods of being a

moderate or heavy drinker rise. When income and education are not controlled for

age groups 35-‐49 and 50-‐64 have higher odds of being moderate or heavy drinkers

compared to 18-‐34 year olds. 65+ have lower odds than 18-‐34 year olds to be

moderate or heavy drinkers. In Model 4 when these variables are controlled for all

age groups have less odds of being moderate of heavy drinkers compared to 18-‐34

year olds, however there is not a significant difference between 18-‐34 year olds and

50-‐64 year olds.

Activity level and drinking are significant at all levels across all models.

Higher activity levels display higher odds of being a moderate or heavy drinker

compared to a non-‐drinker. This pattern remains constant across all controls of age,

sex, race, income, education and self-‐reported health.

DISCUSSION This research has demonstrated that a person’s level of activity can impact

other health behaviors, although it may not be in the direction originally

hypothesized. There is a significant relationship between activity level and doctor

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visits. When controls are set, the significance is only between the two extreme

groups of activity levels, ‘None’ compared to ‘High’. Those who are of ‘Low’ or

‘Moderate’ do not show enough significance to differ from those living a sedentary

lifestyle. The conclusion is that those who are sedentary have higher odds of seeing

a doctor within the past year than those who maintain the highest level of physical

activity.

Smoking and activity level show significance with all levels when no control

variables are set in place. Once demographics are controlled for only one group is

significant to the reference group. ‘Moderate’ activity level is the only significance

compared to ‘None’. Those who have ‘Low’ and ‘High’ activity levels show no

significance to being a non-‐smoker versus a smoker when being compared to ‘None’.

Only those with ‘Moderate’ activity level are less likely to smoke.

All activity levels are significant to drinking. Every level remains significant

through each model as demographics are controlled for. As the level of activity

increases, so do the odds of being a moderate or heavy drinker as opposed to not

drinking. Although this does not meet the original hypothesis, there is still

significance between activity levels and drinking it is just shown in a different

direction than what was expected.

Finding this direction between drinking and physical activity brings up some

questions. Since we see those who are in the highest activity level also being the

most likely drink, is drinking really considered as risky a health behavior as

originally thought? Is there the possibility that moderate consumption may be

healthy which may be why we see those in the highest activity level having the

highest odds of moderate or heavy drinking compared to non-‐drinking? Some

previous studies have suggested a protective effect from drinking light to moderate

amounts of alcohol. They have indicated that that moderate alcohol consumption

can protect against cardiovascular disease. The effect has been demonstrated across

all age ranges and is present for both men and women ( Thakker, 1998).

Future research should examine age groups across a lifetime. This research

examined age groups during the same time period. Examining how a certain age

cohort’s activity and health habits change as they move from 18-‐34 to 65+ could

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prove to be significant for future researchers. It is also important to note the BRFSS

has several limitations which are common among most surveys. Certain problems

may arise, due to inaccuracy or respondents recalling their behavior.

One limitation to reference about this research in particular is the large

number of missing data. The data set originally had 430,912 observations that were

cut down to 268,522. This was caused by the number of participants who did not

answer certain questions that were examined. The largest number came from

58,622 who did not report their household income. Those who did not report

household income were not included in the research. The observations in which

income was not reported had higher percentages of being in the ‘None’ activity level

category than those who did report income. Table 8 displays the descriptive

statistics for those cases which did not report their income. These percentages show

that for activity, the largest percentage missing were ‘None’. Those who had seen a

doctor within the past year, smokers, and heavy drinkers, were all the most likely to

be excluded from this analysis because they did not report their household income.

Age group 65 plus, females, Hispanics, and those with less than a high school

education were all most likely to be excluded due to missing data for household

income.

The research suggests that physical activity can predict health behaviors. The

findings show those who are less active are more likely to have received a medical

exam within the past 12 months, the moderately active are least likely to smoke, and

those who are the most active are also the most likely to be a moderate or heavy

drinker. Although this research alone cannot say higher level of physical activity

produce better health habits, combined with the previous literature it does point us

in the direction of seeing that being physically active can have positive effects on

health habits and healthy lifestyles.

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19

TABLE 1. Percentage Distribution of Medical Exam by Smoking

Non-‐Smoker Smoker

Within Year

76.27 65.94

Longer than 1 Year 23.73 34.06

BRFSS, 2007

TABLE 2. Percentage Distribution of Medical Exam by Drinking

Non-‐Drinker

Moderate Drinker

Heavy Drinker

Within Year

75.65 71.53 66.19

Longer than 1 Year 24.35 28.48 33.81

BRFSS, 2007

TABLE 3. Percentage Distribution of Smoking by Drinking

Non-‐Drinker

Moderate Drinker

Heavy Drinker

Non-‐Smoker

83.68 83.66 65.36

Smoker

16.32 16.34 34.64

BRFSS, 2007

20

Table 4. Percent Distribution of Physical Activity by Social Demographics

ACTIVITY

None Low Moderate High

Sex N

Male 92,633 15.01 45.42 10.97 28.6

Female 175,889 17.86 55.78 8.95 17.42 Race

White 206,728 15.42 52.59 10.05 21.93

Black 23,589 23.1 52.27 7.93 16.7

Hispanic 21,139 24.09 49.77 7.81 18.34

Other 17,066 17.02 50.43 9.33 23.22

Age

18-‐34 34,583 9.9 45.27 14.15 30.68

35-‐49 69,988 11.78 48.53 12.89 26.8

50-‐64 77,600 16.03 53.66 9.6 20.7

65+ 84,106 24.78 56.56 5.19 13.47 Income

less than $10,000 14,575 33.13 51.91 4.36 10.6

$10,000 -‐ $24,999 60,504 24.12 54.84 6.3 14.74

$25,000 -‐ $49,999 30,745 16.94 54.42 8.64 20

$50,000 -‐ $74,999 72,890 11.05 52.21 12.01 24.73

$75,000+ 51,208 6.88 45.89 14.61 32.63 Education

less than High School 32,797 31.36 51.47 4.76 12.4

High School 86,119 19.79 54.15 7.8 18.26

Some College 68,314 14.83 53.25 10.08 21.84 College Graduate 80,353 9.52 49.34 13.33 27.81

BRFSS,2007

21

TABLE 5. Odds Ratios from Logistic Regression on Activity Level and

Medical Visits in Last Year Medical Visits in Last Year

BRFSS,2007

Model 1 p-‐value Model 2 p-‐value Model 3 p-‐value Model 4 p-‐value

Activity Low 0.819 <.0001 0.918 <.0001 1.013 0.3526 0.978 0.1525

Moderate 0.652 <.0001 0.786 <.0001 1.06 0.0024 0.986 0.5023

High 0.638 <.0001 0.773 <.0001 1.038 0.0228 0.963 0.0349

General Health Poor

1.961 <.0001 1.543 <.0001 1.922 <.0001

Fair

1.617 <.0001 1.277 <.0001 1.517 <.0001

Good

1.265 <.0001 1.10 <.0001 1.211 <.0001

Very Good

1.116 <.0001 1.06 <.0001 1.086 <.0001

Age 18-‐34

1.00

1.00

35-‐49

1.16 <.0001 1.101 <.0001

50-‐64

1.862 <.0001 1.1814 <.0001

65+

3.95 <.0001 3.967 <.0001

Sex Female

1.433 <.0001 1.50 <.0001 Race Black

1.888 <.0001 2.111 <.0001

Hispanic

1.138 <.0001 1.3 <.0001

Other

1.045 0.0185 1.092 <.0001

Education High School

1.058 0.0021

Some College

1.039 0.0492

College Gradute

1.069 0.0008

Income $10,000 -‐ $24,999

0.983 0.4633

$25,000 -‐ $49,999

1.124 <.0001

$50,000 -‐ $74,999

1.412 <.0001

$75,000 +

1.775 <.0001

22

TABLE 6. Odds Ratios from Logistic Regression on Physical Activity and Smoking


Activity Low 0.857 <.0001 1.008 0.5921 0.895 <.0001 0.978 0.1708

Moderate 0.688 <.0001 0.915 <.0001 0.678 <.0001 0.822 <.0001

High 0.827 <.0001 1.121 <.0001 0.854 <.0001 1.007 0.7422

General Health Poor

3.006 <.0001 3.995 <.0001 2.278 <.0001

Fair

2.267 <.0001 3.041 <.0001 1.927 <.0001

Good

1.834 <.0001 2.171 <.0001 1.676 <.0001

Very Good

1.442 <.0001 1.528 <.0001 1.403 <.0001

Age 35-‐49

0.989 0.5013 1.119 <.0001

50-‐64

0.74 <.0001 0.814 <.0001

65+

0.279 <.0001 0.253 <.0001

Sex Female

0.87 <.0001 0.825 <.0001 Race Black

0.504 <.0001 0.615 <.0001

Hispanic

0.99 <.0001 0.362 <.0001

Other

0.87 0.6277 0.935 0.0035


0.784 <.0001

Some College

0.703 <.0001

College Graduate

0.357 <.0001

Income $10,000 -‐ $24,999

0.846 <.0001

$25,000 -‐ $49,999

0.679 <.0001

$50,000 -‐ $74,999

0.526 <.0001

$75,000 +

0.348 <.0001

BRFSS, 2007

23

TABLE 7. Odds Ratios from Ordered Logit on Activity Level and Drinking


Activity Low 2.213 <.0001 1.708 <.0001 1.608 <.0001 1.417 <.0001

Moderate 4.084 <.0001 2.656 <.0001 2.209 <.0001 1.754 <.0001

High 4.386 <.0001 2.819 <.0001 2.288 <.0001 1.858 <.0001

General Health Poor

0.188 <.0001 0.196 <.001 0.335 <.0001

Fair

0.292 <.0001 0.322 <.0001 0.499 <.0001

Good

0.524 <.0001 0.55 <.0001 0.71 <.0001

Very Good

0.853 <.0001 0.847 <.0001 0.922 <.0001

Age 35-‐49

1.176 <.0001 .984 0.3238

50-‐64

1.031 0.0498 .891 <.0001

65+

.711 <.0001 .795 <.0001

Sex Female

0.523 <.0001 0.567 <.0001 Race Black

0.462 <.0001 0.579 <.0001

Hispanic

0.531 <.0001 0.757 <.0001

Other

0.546 <.0001 0.568 <.0001


1.317 <.0001

Some College

1.647 <.0001

College Graduate

2.089 <.0001

Income $10,000 -‐ $24,999

1.235 <.001

$25,000 -‐ $49,999

1.584 <.0001

$50,000 -‐ $74,999

2.051 <.0001

$75,000 +

3.401 <.001

BRFSS,2007

24

TABLE 8. Percent Distribution of Reported Household Income Data & Excluded from the Analysis

Activity

None Low Moderate High Reported 79.89 85.10 90.13 89.88 Not Reported 20.11 14.90 9.87 10.12

Medical Exam

Within Year

Longer than 1 Year

Reported 84.91 88.19 Not Reported 15.09 11.81

Smoker

Yes No Reported 85.18 88.77 Not Reported 14.82 11.23

Drinker

No Moderate Heavy Reported 84.47 90.99 92.38 Not Reported 15.53 9.01 7.62

Age

18-‐34 35-‐49 50-‐64 65+ Reported 87.31 91.42 88.72 78.58 Not Reported 12.69 8.58 11.28 21.42

Sex

Male Female Reported 89.58 83.7 Not Reported 10.42 16.3

Race

White Black Hispanic Other Reported 85.97 85.75 84.29 84.5 Not Reported 14.03 14.25 15.71 15.5

Less than HS High School

Some College

College Graduate

Reported 77.74 83.59 87.61 90.17 Not Reported 22.26 16.41 12.39 9.83

BRFSS, 2007