Health Age And Gender

Health, Age and Gender

Contents:

1) Introduction:

2) Theoretical framework:

3) Data:

i) Population and Sampling:

ii) Analysis:

(a) Gender, Health status and Age variables

4) Econometrics models:

i) Simple regression models;

(a) Health status and age:

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Health, Age and Gender

1. Model specification and Expected signs

2. Results:

(b)  Health status and gender:

1. Model specification and Expected signs

2. Results:

ii) Multiple regression models:

(a) Health status, age and gender:

1. Model specification and Expected signs

2. Results:

3. Chow and Breusch Pagan test

5) Conclusion:

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6) References:

7) Appendixes:

1. Introduction:

This paper focuses on health status as related to age and gender, trends in health status as related to age and gender can be associated with social economic status and biological factors. The following is a discussion of the theories that relate to health, age and gender, the discussion shows that women get ill often than men, also that older individuals get ill often than younger age groups. Using data from the Wales health survey website data regarding health status, age and sex is retrieved in order to estimate a model that shows the relationship between health, age and gender.

2. Theoretical framework:

According to Doyle (1995) there is a difference in life expectancy and health status among men and women, in this study he states that men are more likely to die while women are more likely to get ill often, also that women life expectancy was higher than that of men, this means that health status differs between male and female whereby women often get ill but higher life expectancy than men.

The difference in health status between male and female is partially explained by the difference in life expectancy, women are more likely to live longer and given that older people get ill often

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then the majority of individuals who fall ill are women, he points out that women fall ill due to biological factors, these biological factors include the fact that men have greater resistance to diseases than women, the other factor is that women are more likely to report illnesses unlike men. (Steve, 2007)

The difference in health status between men and women is also explained by social economic factors, this include the concept that women are more likely to be poor or have lower levels of income and therefore may fall ill often. The other reason is that women live in a male dominated world and therefore are more likely to be depressed, face domestic violence and other incidences. Men on the other hand are more likely to engage in risky activities such as smoking and drinking and for this reason their life expectancy is lower than that of women. (Doyle, 1995)

With reference to age health status differs across age groups, the population structure is influenced by social economic factors, the dependency ratio which refers to the proportion of the elderly and the labour force is an important measure in the modern society, the old in the society are more likely to have lower income levels than younger individuals, due to this differences then older people are more likely to fall ill often, the other reason is that women comprise of the majority of these old individuals in the society and therefore are more likely to fall ill often. (Steve, 2007)

The other reason why older individuals fall ill often is due to social change that have changed the attitude toward the older individuals, in the modern society due to technological changes older people are not revered due to their experience, they are seen as a burden to the economy because they are unproductive. Differences in health status between the age groups can also be explained by factors such as exercises whereby younger age groups exercise more than older age groups, the other reason is biological factors whereby older people have less resistance to diseases and therefore are more likely to fall ill often. (Steve, 2007)

3. Data:

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Population data:

Data was retrieved from Wales health survey website and is available at wales.gov.uk/docs/statistics/2009/090929hlthsurvey08ch3en.xls, data contains information regarding health status, age and sex, the health variable has five categories and they include excellent, very good, good, fair and poor health status, these categories are assigned number 1 to 5 respectively, therefore this means that the higher the value of health status then the individuals has poor health status.

Age is also has seven age groups and they include those aged 16-44 years, 25-34,45-64 and 65+ years, these age groups are assigned number 1 to 3 respectively whereby a higher age value shows than an individual is older. Finally gender is treated as a dummy variable whereby the value 1 shows that the individual is male and number 0 means the individual is female.

Sample:

The population size N = 13226 and therefore we selected a sample that will represent the entire population; an appropriate sample size is calculated as follows:

n = N / (1+N (e2))

Where n is the sample size, N is the population size and e is the expected error which in this case is 5%, the following is a summary of these calculations: n = 13226 / (1+13226 (0.052)) therefore n = 388.258 and the sample size is assumed to be 400

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A stratified sample is selected which represent all the age groups, sex and health status, from the results there data set is selected and using STATA the following are the results:

Analysis:

Gender:

Given that the population male and female number is almost equal the sample selected contains 200 male and 200 female respondents, given that 1 represents male and 0 represents female and that 1 depicts excellent health status and 5 poor health status then the following table summarizes the relationship between gender and health:

health

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gender

1

2

3

4

5

0

28

65

60

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35

12

1

32

66

58

29

15

From the table there are more male than female with excellent health status, however there are male than female with poor health status.

The above frequency values are summarised in the following bar charts

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The chart above summarises health status of male respondents:

The chart above summarises health status of male respondents:

From the two charts there are more female participants with health status value of 2 and 3 compared to male health status chart.

Health status:

Health status is another variable in the data; this variable shows the health status of the respondents, from the table below majority of the individuals have good health status with only 27 individuals with poor health status, the table below summarizes the results:

health

Freq.

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1

60

2

131

3

118

4

64

5

27

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The chart below summaries the percentage number of Individuals classified with reference to health status;

From the chart 32.7% respondents had good health status, only 6.75% respondents had poor health status and only 15% had excellent health status.

Age:

Age groups was also another variable in the data, the value 1 represented those aged 16 – 44, 2 represented those aged between 45 – 64 and 3 represented those aged 65 and above. The table below summarizes the results:

age

Freq.

1

155

2

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140

3

105

From the table majority of the individuals were aged 16 to 44 years while only 105 were aged 65 years and above, this means that majority of the respondents were young while only a few were older. The chart below summarizes the results:

The chart shows that 38.75% of the respondents were aged 16 to 44 years, 35% were aged 45 to 64 years and 26.25% were aged 65 years and above.

Health status and gender:

The relationship between health and age can be analyzed using a scatter diagram; the diagram below shows the relationship between the two variables:

From the chart as the age scale increases then the health value also increases, from the health scale as the health status value increases then this means that health is deteriorating whereby value 1 means that an individual has excellent health while value 5 shows that an individual has poor health.

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4. Econometric model: Simple regression:

In this section the simple regression models are estimated to show the relationship between health, age and gender, using the above theories the models are specified as follows:

Health status and gender:

The model is specified as follows:

Health status = a1 + b1 gender + Ei

The value of b1 is expected to be negative given that the value of gender 1 means that the respondent is male, male individuals will yield a lower level of health status as stated in the theoretical framework whereby women are more likely to get ill often than men. The value of a1 is expected to be positive given that health status should be positive even when the value of gender is zero.

Results:

The table below summarizes the results:

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Number of obs

= 400

F( 1, 398)

= 0.16

Prob > F

= 0.687

R-squared

= 0.0004

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Health, Age and Gender

Adj R-squared

= -0.002

health

Coef.

Std. Err.                         t

P>t

[95% Conf.

Interval

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Health, Age and Gender

gender

-.045

.1119241                   -0.40

0.688

-.2650364

.175036

_cons

2.69

.0791423                     33.99

0.000

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2.534411

2.84558

From the above results the estimated model is as follows:

Health status = 2.69 -0.045 gender

The model states that when gender = 1(male) then the value of health status declines by

-0.045(move toward the value 1 which is excellent health status), if the gender value is zero (female = 0) then the health status value is 2.69.

Hypothesis test:

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Gender coefficient:

Null hypothesis: B1 = 0, alternative hypothesis B1 ≠ 0

Given that the T statistics value < T critical value, then the null hypothesis is accepted, this means the gender coefficient is not statistically significant

Constant value:

Null hypothesis: a1 = 0, alternative hypothesis a1 ≠ 0

Given that the T statistics value > T critical value, then the null hypothesis is rejected, this means the constant is statistically significant

Health status and age:

The model is specified as follows:

Health status = a2 + b2 age + Ei

The value of b2 is expected to be positive given that as age increases health status also increases, the value of a2 is expected to be positive given that health status should be positive

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even when the value of age is zero.

Results:

The table below summarizes the results:

Number of obs

= 140

F( 1, 1403)

= 258.05

Model

266.062098

1 266.062098

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Prob > F

= 0.0000

Residual

1446.53861

1403  1.03103251

R-squared

= 0.1554

Adj R-squared

= 0.1548

Total

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1712.60071

1404  1.21980108

Root MSE

= 1.0154

Health status

Coef.

Std. Err.                          t

P>t

[95% Conf.

Interval]

age

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.2170625

.0135123                 16.06

0.000

.190556

.243569

_cons

1.770863

.0603805                 29.33

0.000

1.652417

1.889309

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From the table the model is specified as follows

Health status = 1.770863 + 0.217 age

The above model states that as age increases then health status increases, an increase in the value of health status means that an individual will move from excellent health status to poor health status, the R squared value of the model is 0.1554 meaning that 15.54% deviations in health status is explained by age.

Hypothesis test:

Age coefficient:

Null hypothesis: B2 = 0, alternative hypothesis B2 ≠ 0

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Given that the T statistics value > T critical value, then the null hypothesis is rejected, this means the age coefficient is statistically significant

Constant value:

Null hypothesis: a1 = 0, alternative hypothesis a1 ≠ 0

Given that the T statistics value > T critical value, then the null hypothesis is rejected, this means the constant is statistically significant

Multiple regressions:

This section estimates a multiple regression model where age and gender are independent variables and health status is the dependent variable;

Model specification;

Models are specified with reference to existing theories, using our discussion on theories that depict the relationship between health, age and gender the model is specified as follows:

Health status = a1 + b1age + b2 gender

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Expected signs:

We expect that the value of b1 will be positive, this will mean that as age increases then health status increases from excellent to poor, the value of b2 is expected to negative and this means that when the value of gender is one which means the respondent is male then health status is expected to have a lower value (health scale: 1 excellent and 5 poor)

Results:

The table below summarizes the results:

Number of obs

= 400

F( 2, 397)

= 28.25

Prob > F

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= 0.0000

R-squared

= 0.1246

Adj R-squared

= 0.1202

health

Coef.

Std. Err. t

P>t

[95% Conf.

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Interval]

gender

-.045

.1048723 -0.43

0.668

-.2511745

.1611745

age

.4940887

.0658351 7.50

0.000

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.3646596

.6235178

_cons

1.763584

.1440026 12.25

0.000

1.480481

2.046687

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From the table the model can be stated as follows:

Health status = a1 + 0.4940887age -0.045gender

The above model states that when gender is male then health status declines, also when the age value is increased the health status increases.

Hypothesis test:

Age coefficient:

Null hypothesis: B1 = 0, alternative hypothesis B1 ≠ 0

Given that the T statistics value > T critical value, then the null hypothesis is rejected, this means the age coefficient is statistically significant

Gender coefficient:

Null hypothesis: B2 = 0, alternative hypothesis B2 ≠ 0

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Given that the T statistics value < T critical value, then the null hypothesis is accepted, this means the gender coefficient is not statistically significant

Constant value:

Null hypothesis: a1 = 0, alternative hypothesis a1 ≠ 0

Given that the T statistics value > T critical value, then the null hypothesis is rejected, this means the constant is statistically significant

F test:

Null hypothesis: a1 =b1=b2 = 0, alternative hypothesis a1 ≠ b1 ≠ b2 ≠ 0

Given that the F statistics value > F critical value, then the null hypothesis is rejected; this means the coefficients are statistically significant.

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Chow test;

Given that we have male and female we test whether the two group coefficients are equal, this entails determining whether the age coefficient estimated using male only data is equal to the coefficient estimated using female only data:

The estimated model is as follows:

Health = b1 age + Ei

The above model is split into two models:

Health1 = a1 + b1 age1 + Ei (male group data)

Health2 = a1 + b2 age2 + Eii (female group data)

The two equations are then combined as follows:

H = D1 ( b1 age1 + Ei) + D2(b2 age2 + Eii)

Where D1 = 1 when considering male and zero for the female group data, D2 i=1 when considering female data and zero when considering male data.

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Therefore:

H = D1b1age1 + D1Ei + D2b2age2 + D2Eii

And:

H = b1(D1age1) + b2(D2age2+ D1Ei+ D2Eii

This is the model to be estimated, two groups are formed which is group 1 and group 2, group 1 contain data where respondents are male and group 2 contains data whose gender is female, also new variables are generated whereby age1 = age * group1 and age 2 = age* group 2. The following table summarises the estimated of the above model:

Number of obs

= 400

F( 2, 398)

= 871.01

Prob > F

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= 0.0000

R-squared

= 0.8140

Adj R-squared

= 0.8131

health

Coef.

Std. Err. t

P>t

[95% Conf.

Interval]

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age1

1.273824

.0434223 29.34

0.000

1.188458

1.35919

age2

1.287605

.04337 29.69

0.000

1.202342

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1.372868

The estimated model is stated as follows:

H = 1.273824D1age1 + 1.287605D2age2

We now test whether b1 = b2 = 0 using STATA, the following table shows the results:

. test

age1=age2

( 1)

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age1 – age2 =0

F( 1, 398)= 0.05

Prob > F =0.8224

From the F test the null hypothesis that the two coefficients are equal is accepted, this means that the coefficient is equal for both male and female participants. This means that the impact of age on health status for both male and female is relatively equal.

Heteroskedasticity:

One assumptions of the linear regression model is that the variance of the error term is constant across observations, when this assumption is violated then we have Heteroskedasticity. Consequences are that the estimated coefficients are biased and the estimated values of

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standard errors are biased. The following table summarises results for the Breusch Pagan test for Heteroskedasticity

Breusch-Pagan / Cook-Weisberg test for Heteroskedasticity

Ho: Constant variance

Variables: fitted values of health

chi2(1) = 1.33

Prob > chi2 = 0.2480

The null hypothesis that the variance is constant is accepted and therefore the error term has a constant variance.

6. Conclusion:

Theories have been developed to explain the relationship between age and health status, health and gender, from these theories some argue that women are more likely to get ill than mean, analyzing the relationship between gender and health status show that male are more likely to have better health status than women.

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Age is also a factor that influence health status, as individuals grow old their health status deteriorates and this is depicted by the regression showing the relationship between age and health where as age increases the health status shifts to poor health status.

The Chow test on the equality of the age coefficient for both female and male groups shows that the coefficients are equal, the null hypothesis that the two coefficients are equal is accepted, and this means that the coefficients are equal for both male and female participants. This means that the impact of age on health status for both male and female is relatively equal. Other studies should aim at determine other factors that influence the health of individuals.

7. References:

Doyle, L. (1995). What makes women sick? Macmillan, London.

Coleman, et al. (1993). Ageing in the twentieth century, Sage: London

Liu H and Shaffer D. (2004). The Effects of Gender and Age on Health. Macmillan, London.

Steve Brindle (2007). Gender, Health and Age, retrieved on 7th January, from <www.abdn.ac.uk/public_health/genderagehealth.html>

Wales Health statistics (2008). Health survey data 2008, retrieved on 7th January, from

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<wales.gov.uk/docs/statistics/2009/090929hlthsurvey08ch3en.xls>

8. Appendixes

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log: C:\Documents  and Settings\Administrator\Deskto

> p\2222.smcl

log type: smcl

opened on: 7 Jan  2010, 04:27:08

. table health

health Freq.

1 60

2 131

3 118

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4 64

5 27

. table gender  health

health

gender 1 2 3 4 5

02865603512

13266582915

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. table age health

health

age 1 2 3 4 5

1386339114

21646452310

3622343013

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. graph pie,  over(age) title(Age)

. graph pie,  over(age) title(Age) plabel(_all percent)

. graph pie,  over(health) title(Age) plabel(_all percent)

. graph pie,  over(health) title(Health) plabel(_all percent

> )

. table health

health Freq.

1 60

2 131

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3 118

4 64

5 27

. table age

age Freq.

1 155

2 140

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Health, Age and Gender

3 105

. save  "C:\Documents and Settings\Administrator\Desktop\dat

> a  used.dta", replace

file C:\Documents  and Settings\Administrator\Desktop\data u

> sed.dta  saved

. twoway  connected health age

. scatter health  age, sort

. regress health  age gender

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Health, Age and Gender

Source SS df MS  Numb

> er of obs =  400

F(

> 2, 397) =  28.25

Model 62.148867 2  31.0744335 Prob

> > F =  0.0000

Residual  436.628633 397 1.09982023 R-sq

> uared =  0.1246

Adj

> R-squared =  0.1202

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Health, Age and Gender

Total 498.7775  399 1.25006892 Root

> MSE = 1.0487

>  ——————-

health Coef. Std.  Err. t P>t [

> 95% Conf.  Interval]

>  ——————-

age .4940887  .0658351 7.50 0.000 .

> 3646596  .6235178

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Health, Age and Gender

gender -.045  .1048723 -0.43 0.668 -.

> 2511745  .1611745

_cons 1.763584  .1440026 12.25 0.000 1

> .480481  2.046687

> ——————-

. regress health  age

Source SS df MS  Numb

> er of obs =  400

F(

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Health, Age and Gender

> 1, 398) =  56.44

Model 61.946367 1  61.946367 Prob

> > F =  0.0000

Residual  436.831133 398 1.09756566 R-sq

> uared =  0.1242

Adj

> R-squared =  0.1220

Total 498.7775  399 1.25006892 Root

> MSE = 1.0476

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>  ——————-

health Coef. Std.  Err. t P>t [

> 95% Conf.  Interval]

>  ——————-

age .4940887  .0657676 7.51 0.000 .

> 3647933  .623384

_cons 1.741084  .1339789 13.00 0.000 1

> .477689  2.004478

> ——————-

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Health, Age and Gender

. regress health  gender

Source SS df MS  Numb

> er of obs =  400

F(

> 1, 398) =  0.16

Model .2025 1  .2025 Prob

> > F =  0.6879

Residual 498.575  398 1.25270101 R-sq

> uared =  0.0004

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Health, Age and Gender

Adj

> R-squared =  -0.0021

Total 498.7775  399 1.25006892 Root

> MSE = 1.1192

>  ——————-

health Coef. Std.  Err. t P>t [

> 95% Conf.  Interval]

>  ——————-

gender -.045  .1119241 -0.40 0.688 -.

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Health, Age and Gender

> 2650364  .1750364

_cons 2.69  .0791423 33.99 0.000 2

> .534411  2.845589

> ——————-

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